Yuanbin Wu


2023

pdf bib
A Confidence-based Partial Label Learning Model for Crowd-Annotated Named Entity Recognition
Limao Xiong | Jie Zhou | Qunxi Zhu | Xiao Wang | Yuanbin Wu | Qi Zhang | Tao Gui | Xuanjing Huang | Jin Ma | Ying Shan
Findings of the Association for Computational Linguistics: ACL 2023

Existing models for named entity recognition (NER) are mainly based on large-scale labeled datasets, which always obtain using crowdsourcing. However, it is hard to obtain a unified and correct label via majority voting from multiple annotators for NER due to the large labeling space and complexity of this task. To address this problem, we aim to utilize the original multi-annotator labels directly. Particularly, we propose a CONfidence-based partial Label Learning (CONLL) method to integrate the prior confidence (given by annotators) and posterior confidences (learned by models) for crowd-annotated NER. This model learns a token- and content-dependent confidence via an Expectation–Maximization (EM) algorithm by minimizing empirical risk. The true posterior estimator and confidence estimator perform iteratively to update the true posterior and confidence respectively. We conduct extensive experimental results on both real-world and synthetic datasets, which show that our model can improve performance effectively compared with strong baselines.

pdf bib
Typology Guided Multilingual Position Representations: Case on Dependency Parsing
Tao Ji | Yuanbin Wu | Xiaoling Wang
Findings of the Association for Computational Linguistics: ACL 2023

Recent multilingual models benefit from strong unified semantic representation models. However, due to conflict linguistic regularities, ignoring language-specific features during multilingual learning may suffer from negative transfer. In this work, we analyze the relationbetween a language’s position space and its typological characterization, and suggest deploying different position spaces for different languages. We develop a position generation network which combines prior knowledge from typology features and existing position vectors. Experiments on the multilingual dependency parsing task show that the learned position vectors exhibit meaningful hidden structures, and they can help achieving the best multilingual parsing results.

pdf bib
A Multi-Task Dataset for Assessing Discourse Coherence in Chinese Essays: Structure, Theme, and Logic Analysis
Hongyi Wu | Xinshu Shen | Man Lan | Shaoguang Mao | Xiaopeng Bai | Yuanbin Wu
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

This paper introduces the Chinese Essay Discourse Coherence Corpus (CEDCC), a multi-task dataset for assessing discourse coherence. Existing research tends to focus on isolated dimensions of discourse coherence, a gap which the CEDCC addresses by integrating coherence grading, topical continuity, and discourse relations. This approach, alongside detailed annotations, captures the subtleties of real-world texts and stimulates progress in Chinese discourse coherence analysis. Our contributions include the development of the CEDCC, the establishment of baselines for further research, and the demonstration of the impact of coherence on discourse relation recognition and automated essay scoring. The dataset and related codes is available at https://github.com/cubenlp/CEDCC_corpus.

pdf bib
Overview of CCL23-Eval Task 8: Chinese Essay Fluency Evaluation (CEFE) Task
Xinshu Shen | Hongyi Wu | Xiaopeng Bai | Yuanbin Wu | Aimin Zhou | Shaoguang Mao | Tao Ge | Yan Xia
Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)

“This paper provides a comprehensive review of the CCL23-Eval Task 8, i.e., Chinese EssayFluency Evaluation (CEFE). The primary aim of this task is to systematically identify the typesof grammatical fine-grained errors that affect the readability and coherence of essays writtenby Chinese primary and secondary school students, and then to suggest suitable corrections toenhance the fluidity of their written expression. This task consists of three distinct tracks: (1)Coarse-grained and fine-grained error identification; (2) Character-level error identification andcorrection; (3) Error sentence rewriting. In the end, we received 44 completed registration forms,leading to a total of 130 submissions from 11 dedicated participating teams. We present theresults of all participants and our analysis of these results. Both the dataset and evaluation toolused in this task are available1.”

pdf bib
Connective Prediction for Implicit Discourse Relation Recognition via Knowledge Distillation
Hongyi Wu | Hao Zhou | Man Lan | Yuanbin Wu | Yadong Zhang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis due to the absence of connectives. Most existing methods utilize one-hot labels as the sole optimization target, ignoring the internal association among connectives. Besides, these approaches spend lots of effort on template construction, negatively affecting the generalization capability. To address these problems,we propose a novel Connective Prediction via Knowledge Distillation (CP-KD) approach to instruct large-scale pre-trained language models (PLMs) mining the latent correlations between connectives and discourse relations, which is meaningful for IDRR. Experimental results on the PDTB 2.0/3.0 and CoNLL2016 datasets show that our method significantly outperforms the state-of-the-art models on coarse-grained and fine-grained discourse relations. Moreover, our approach can be transferred to explicit discourse relation recognition(EDRR) and achieve acceptable performance.

pdf bib
Rehearsal-free Continual Language Learning via Efficient Parameter Isolation
Zhicheng Wang | Yufang Liu | Tao Ji | Xiaoling Wang | Yuanbin Wu | Congcong Jiang | Ye Chao | Zhencong Han | Ling Wang | Xu Shao | Wenqiu Zeng
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

We study the problem of defying catastrophic forgetting when learning a series of language processing tasks. Compared with previous methods, we emphasize the importance of not caching history tasks’ data, which makes the problem more challenging. Our proposed method applies the parameter isolation strategy. For each task, it allocates a small portion of private parameters and learns them with a shared pre-trained model. To load correct parameters at testing time, we introduce a simple yet effective non-parametric method. Experiments on continual language learning benchmarks show that our method is significantly better than all existing no-data-cache methods, and is comparable (or even better) than those using historical data.

pdf bib
CodeIE: Large Code Generation Models are Better Few-Shot Information Extractors
Peng Li | Tianxiang Sun | Qiong Tang | Hang Yan | Yuanbin Wu | Xuanjing Huang | Xipeng Qiu
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Large language models (LLMs) pre-trained on massive corpora have demonstrated impressive few-shot learning ability on many NLP tasks. A common practice is to recast the task into a text-to-text format such that generative LLMs of natural language (NL-LLMs) like GPT-3 can be prompted to solve it. However, it is nontrivial to perform information extraction (IE) tasks with NL-LLMs since the output of the IE task is usually structured and therefore is hard to be converted into plain text. In this paper, we propose to recast the structured output in the form of code instead of natural language and utilize generative LLMs of code (Code-LLMs) such as Codex to perform IE tasks, in particular, named entity recognition and relation extraction. In contrast to NL-LLMs, we show that Code-LLMs can be well-aligned with these IE tasks by designing code-style prompts and formulating these IE tasks as code generation tasks. Experiment results on seven benchmarks show that our method consistently outperforms fine-tuning moderate-size pre-trained models specially designed for IE tasks (e.g., UIE) and prompting NL-LLMs under few-shot settings. We further conduct a series of in-depth analyses to demonstrate the merits of leveraging Code-LLMs for IE tasks.

2022

pdf bib
Prompt-based Connective Prediction Method for Fine-grained Implicit Discourse Relation Recognition
Hao Zhou | Man Lan | Yuanbin Wu | Yuefeng Chen | Meirong Ma
Findings of the Association for Computational Linguistics: EMNLP 2022

Due to the absence of connectives, implicit discourse relation recognition (IDRR) is still a challenging and crucial task in discourse analysis. Most of the current work adopted multitask learning to aid IDRR through explicit discourse relation recognition (EDRR) or utilized dependencies between discourse relation labels to constrain model predictions. But these methods still performed poorly on fine-grained IDRR and even utterly misidentified on most of the few-shot discourse relation classes. To address these problems, we propose a novel Prompt-based Connective Prediction (PCP) method for IDRR. Our method instructs large-scale pre-trained models to use knowledge relevant to discourse relation and utilizes the strong correlation between connectives and discourse relation to help the model recognize implicit discourse relations. Experimental results show that our method surpasses the current state-of-the-art model and achieves significant improvements on those fine-grained few-shot discourse relation. Moreover, our approach is able to be transferred to EDRR and obtain acceptable results. Our code is released in https://github.com/zh-i9/PCP-for-IDRR.

pdf bib
Explore Unsupervised Structures in Pretrained Models for Relation Extraction
Xi Yang | Tao Ji | Yuanbin Wu
Findings of the Association for Computational Linguistics: EMNLP 2022

Syntactic trees have been widely applied in relation extraction (RE). However, since parsing qualities are not stable on different text domains and a pre-defined grammar may not well fit the target relation schema, the introduction of syntactic structures sometimes fails to improve RE performances consistently. In this work, we study RE models with various unsupervised structures mined from pre-trained language models (e.g., BERT). We show that, similar to syntactic trees, unsupervised structures are quite informative for RE task: they are able to obtain competitive (even the best) performance scores on benchmark RE datasets (ACE05, WebNLG, SciERC). We also conduct detailed analyses on their abilities of adapting new RE domains and influence of noise links in those structures. The results suggest that unsupervised structures are reasonable alternatives of commonly used syntactic structures in relation extraction models.

pdf bib
Understanding Gender Bias in Knowledge Base Embeddings
Yupei Du | Qi Zheng | Yuanbin Wu | Man Lan | Yan Yang | Meirong Ma
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Knowledge base (KB) embeddings have been shown to contain gender biases. In this paper, we study two questions regarding these biases: how to quantify them, and how to trace their origins in KB? Specifically, first, we develop two novel bias measures respectively for a group of person entities and an individual person entity. Evidence of their validity is observed by comparison with real-world census data. Second, we use the influence function to inspect the contribution of each triple in KB to the overall group bias. To exemplify the potential applications of our study, we also present two strategies (by adding and removing KB triples) to mitigate gender biases in KB embeddings.

pdf bib
Few Clean Instances Help Denoising Distant Supervision
Yufang Liu | Ziyin Huang | Yijun Wang | Changzhi Sun | Man Lan | Yuanbin Wu | Xiaofeng Mou | Ding Wang
Proceedings of the 29th International Conference on Computational Linguistics

Existing distantly supervised relation extractors usually rely on noisy data for both model training and evaluation, which may lead to garbage-in-garbage-out systems. To alleviate the problem, we study whether a small clean dataset could help improve the quality of distantly supervised models. We show that besides getting a more convincing evaluation of models, a small clean dataset also helps us to build more robust denoising models. Specifically, we propose a new criterion for clean instance selection based on influence functions. It collects sample-level evidence for recognizing good instances (which is more informative than loss-level evidence). We also propose a teacher-student mechanism for controlling purity of intermediate results when bootstrapping the clean set. The whole approach is model-agnostic and demonstrates strong performances on both denoising real (NYT) and synthetic noisy datasets.

pdf bib
LightEA: A Scalable, Robust, and Interpretable Entity Alignment Framework via Three-view Label Propagation
Xin Mao | Wenting Wang | Yuanbin Wu | Man Lan
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Entity Alignment (EA) aims to find equivalent entity pairs between KGs, which is the core step to bridging and integrating multi-source KGs. In this paper, we argue that existing complex EA methods inevitably inherit the inborn defects from their neural network lineage: poor interpretability and weak scalability. Inspired by recent studies, we reinvent the classical Label Propagation algorithm to effectively run on KGs and propose a neural-free EA framework — LightEA, consisting of three efficient components: (i) Random Orthogonal Label Generation, (ii) Three-view Label Propagation, and (iii) Sparse Sinkhorn Operation.According to the extensive experiments on public datasets, LightEA has impressive scalability, robustness, and interpretability. With a mere tenth of time consumption, LightEA achieves comparable results to state-of-the-art methods across all datasets and even surpasses them on many. Besides, due to the computational process of LightEA being entirely linear, we could trace the propagation process at each step and clearly explain how the entities are aligned.

2021

pdf bib
Is “hot pizza” Positive or Negative? Mining Target-aware Sentiment Lexicons
Jie Zhou | Yuanbin Wu | Changzhi Sun | Liang He
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Modelling a word’s polarity in different contexts is a key task in sentiment analysis. Previous works mainly focus on domain dependencies, and assume words’ sentiments are invariant within a specific domain. In this paper, we relax this assumption by binding a word’s sentiment to its collocation words instead of domain labels. This finer view of sentiment contexts is particularly useful for identifying commonsense sentiments expressed in neural words such as “big” and “long”. Given a target (e.g., an aspect), we propose an effective “perturb-and-see” method to extract sentiment words modifying it from large-scale datasets. The reliability of the obtained target-aware sentiment lexicons is extensively evaluated both manually and automatically. We also show that a simple application of the lexicon is able to achieve highly competitive performances on the unsupervised opinion relation extraction task.

pdf bib
ENPAR:Enhancing Entity and Entity Pair Representations for Joint Entity Relation Extraction
Yijun Wang | Changzhi Sun | Yuanbin Wu | Hao Zhou | Lei Li | Junchi Yan
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Current state-of-the-art systems for joint entity relation extraction (Luan et al., 2019; Wad-den et al., 2019) usually adopt the multi-task learning framework. However, annotations for these additional tasks such as coreference resolution and event extraction are always equally hard (or even harder) to obtain. In this work, we propose a pre-training method ENPAR to improve the joint extraction performance. ENPAR requires only the additional entity annotations that are much easier to collect. Unlike most existing works that only consider incorporating entity information into the sentence encoder, we further utilize the entity pair information. Specifically, we devise four novel objectives,i.e., masked entity typing, masked entity prediction, adversarial context discrimination, and permutation prediction, to pre-train an entity encoder and an entity pair encoder. Comprehensive experiments show that the proposed pre-training method achieves significant improvement over BERT on ACE05, SciERC, and NYT, and outperforms current state-of-the-art on ACE05.

pdf bib
Attending via both Fine-tuning and Compressing
Jie Zhou | Yuanbin Wu | Qin Chen | Xuanjing Huang | Liang He
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
Probabilistic Graph Reasoning for Natural Proof Generation
Changzhi Sun | Xinbo Zhang | Jiangjie Chen | Chun Gan | Yuanbin Wu | Jiaze Chen | Hao Zhou | Lei Li
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

pdf bib
From Alignment to Assignment: Frustratingly Simple Unsupervised Entity Alignment
Xin Mao | Wenting Wang | Yuanbin Wu | Man Lan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Cross-lingual entity alignment (EA) aims to find the equivalent entities between crosslingual KGs (Knowledge Graphs), which is a crucial step for integrating KGs. Recently, many GNN-based EA methods are proposed and show decent performance improvements on several public datasets. However, existing GNN-based EA methods inevitably inherit poor interpretability and low efficiency from neural networks. Motivated by the isomorphic assumption of GNN-based methods, we successfully transform the cross-lingual EA problem into an assignment problem. Based on this re-definition, we propose a frustratingly Simple but Effective Unsupervised entity alignment method (SEU) without neural networks. Extensive experiments have been conducted to show that our proposed unsupervised approach even beats advanced supervised methods across all public datasets while having high efficiency, interpretability, and stability.

pdf bib
Word Reordering for Zero-shot Cross-lingual Structured Prediction
Tao Ji | Yong Jiang | Tao Wang | Zhongqiang Huang | Fei Huang | Yuanbin Wu | Xiaoling Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Adapting word order from one language to another is a key problem in cross-lingual structured prediction. Current sentence encoders (e.g., RNN, Transformer with position embeddings) are usually word order sensitive. Even with uniform word form representations (MUSE, mBERT), word order discrepancies may hurt the adaptation of models. In this paper, we build structured prediction models with bag-of-words inputs, and introduce a new reordering module to organizing words following the source language order, which learns task-specific reordering strategies from a general-purpose order predictor model. Experiments on zero-shot cross-lingual dependency parsing, POS tagging, and morphological tagging show that our model can significantly improve target language performances, especially for languages that are distant from the source language.

pdf bib
A Unified Encoding of Structures in Transition Systems
Tao Ji | Yong Jiang | Tao Wang | Zhongqiang Huang | Fei Huang | Yuanbin Wu | Xiaoling Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Transition systems usually contain various dynamic structures (e.g., stacks, buffers). An ideal transition-based model should encode these structures completely and efficiently. Previous works relying on templates or neural network structures either only encode partial structure information or suffer from computation efficiency. In this paper, we propose a novel attention-based encoder unifying representation of all structures in a transition system. Specifically, we separate two views of items on structures, namely structure-invariant view and structure-dependent view. With the help of parallel-friendly attention network, we are able to encoding transition states with O(1) additional complexity (with respect to basic feature extractors). Experiments on the PTB and UD show that our proposed method significantly improves the test speed and achieves the best transition-based model, and is comparable to state-of-the-art methods.

pdf bib
UniRE: A Unified Label Space for Entity Relation Extraction
Yijun Wang | Changzhi Sun | Yuanbin Wu | Hao Zhou | Lei Li | Junchi Yan
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Many joint entity relation extraction models setup two separated label spaces for the two sub-tasks (i.e., entity detection and relation classification). We argue that this setting may hinder the information interaction between entities and relations. In this work, we propose to eliminate the different treatment on the two sub-tasks’ label spaces. The input of our model is a table containing all word pairs from a sentence. Entities and relations are represented by squares and rectangles in the table. We apply a unified classifier to predict each cell’s label, which unifies the learning of two sub-tasks. For testing, an effective (yet fast) approximate decoder is proposed for finding squares and rectangles from tables. Experiments on three benchmarks (ACE04, ACE05, SciERC) show that, using only half the number of parameters, our model achieves competitive accuracy with the best extractor, and is faster.

2020

pdf bib
Pre-training Entity Relation Encoder with Intra-span and Inter-span Information
Yijun Wang | Changzhi Sun | Yuanbin Wu | Junchi Yan | Peng Gao | Guotong Xie
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

In this paper, we integrate span-related information into pre-trained encoder for entity relation extraction task. Instead of using general-purpose sentence encoder (e.g., existing universal pre-trained models), we introduce a span encoder and a span pair encoder to the pre-training network, which makes it easier to import intra-span and inter-span information into the pre-trained model. To learn the encoders, we devise three customized pre-training objectives from different perspectives, which target on tokens, spans, and span pairs. In particular, a span encoder is trained to recover a random shuffling of tokens in a span, and a span pair encoder is trained to predict positive pairs that are from the same sentences and negative pairs that are from different sentences using contrastive loss. Experimental results show that the proposed pre-training method outperforms distantly supervised pre-training, and achieves promising performance on two entity relation extraction benchmark datasets (ACE05, SciERC).

pdf bib
A Span-based Linearization for Constituent Trees
Yang Wei | Yuanbin Wu | Man Lan
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

We propose a novel linearization of a constituent tree, together with a new locally normalized model. For each split point in a sentence, our model computes the normalizer on all spans ending with that split point, and then predicts a tree span from them. Compared with global models, our model is fast and parallelizable. Different from previous local models, our linearization method is tied on the spans directly and considers more local features when performing span prediction, which is more interpretable and effective. Experiments on PTB (95.8 F1) and CTB (92.4 F1) show that our model significantly outperforms existing local models and efficiently achieves competitive results with global models.

pdf bib
SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis
Jie Zhou | Junfeng Tian | Rui Wang | Yuanbin Wu | Wenming Xiao | Liang He
Proceedings of the 28th International Conference on Computational Linguistics

Pre-trained language models have been widely applied to cross-domain NLP tasks like sentiment analysis, achieving state-of-the-art performance. However, due to the variety of users’ emotional expressions across domains, fine-tuning the pre-trained models on the source domain tends to overfit, leading to inferior results on the target domain. In this paper, we pre-train a sentiment-aware language model (SentiX) via domain-invariant sentiment knowledge from large-scale review datasets, and utilize it for cross-domain sentiment analysis task without fine-tuning. We propose several pre-training tasks based on existing lexicons and annotations at both token and sentence levels, such as emoticons, sentiment words, and ratings, without human interference. A series of experiments are conducted and the results indicate the great advantages of our model. We obtain new state-of-the-art results in all the cross-domain sentiment analysis tasks, and our proposed SentiX can be trained with only 1% samples (18 samples) and it achieves better performance than BERT with 90% samples.

2019

pdf bib
Joint Type Inference on Entities and Relations via Graph Convolutional Networks
Changzhi Sun | Yeyun Gong | Yuanbin Wu | Ming Gong | Daxin Jiang | Man Lan | Shiliang Sun | Nan Duan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We develop a new paradigm for the task of joint entity relation extraction. It first identifies entity spans, then performs a joint inference on entity types and relation types. To tackle the joint type inference task, we propose a novel graph convolutional network (GCN) running on an entity-relation bipartite graph. By introducing a binary relation classification task, we are able to utilize the structure of entity-relation bipartite graph in a more efficient and interpretable way. Experiments on ACE05 show that our model outperforms existing joint models in entity performance and is competitive with the state-of-the-art in relation performance.

pdf bib
Graph-based Dependency Parsing with Graph Neural Networks
Tao Ji | Yuanbin Wu | Man Lan
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

We investigate the problem of efficiently incorporating high-order features into neural graph-based dependency parsing. Instead of explicitly extracting high-order features from intermediate parse trees, we develop a more powerful dependency tree node representation which captures high-order information concisely and efficiently. We use graph neural networks (GNNs) to learn the representations and discuss several new configurations of GNN’s updating and aggregation functions. Experiments on PTB show that our parser achieves the best UAS and LAS on PTB (96.0%, 94.3%) among systems without using any external resources.

pdf bib
Exploring Human Gender Stereotypes with Word Association Test
Yupei Du | Yuanbin Wu | Man Lan
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Word embeddings have been widely used to study gender stereotypes in texts. One key problem regarding existing bias scores is to evaluate their validities: do they really reflect true bias levels? For a small set of words (e.g. occupations), we can rely on human annotations or external data. However, for most words, evaluating the correctness of them is still an open problem. In this work, we utilize word association test, which contains rich types of word connections annotated by human participants, to explore how gender stereotypes spread within our minds. Specifically, we use random walk on word association graph to derive bias scores for a large amount of words. Experiments show that these bias scores correlate well with bias in the real world. More importantly, comparing with word-embedding-based bias scores, it provides a different perspective on gender stereotypes in words.

2018

pdf bib
AntNLP at CoNLL 2018 Shared Task: A Graph-Based Parser for Universal Dependency Parsing
Tao Ji | Yufang Liu | Yijun Wang | Yuanbin Wu | Man Lan
Proceedings of the CoNLL 2018 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We describe the graph-based dependency parser in our system (AntNLP) submitted to the CoNLL 2018 UD Shared Task. We use bidirectional lstm to get the word representation, then a bi-affine pointer networks to compute scores of candidate dependency edges and the MST algorithm to get the final dependency tree. From the official testing results, our system gets 70.90 LAS F1 score (rank 9/26), 55.92 MLAS (10/26) and 60.91 BLEX (8/26).

pdf bib
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models
Huimin Xu | Man Lan | Yuanbin Wu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our submissions to SemEval 2018 task 1. The task is affect intensity prediction in tweets, including five subtasks. We participated in all subtasks of English tweets. We extracted several traditional NLP, sentiment lexicon, emotion lexicon and domain specific features from tweets, adopted supervised machine learning algorithms to perform emotion intensity prediction.

pdf bib
ECNU at SemEval-2018 Task 2: Leverage Traditional NLP Features and Neural Networks Methods to Address Twitter Emoji Prediction Task
Xingwu Lu | Xin Mao | Man Lan | Yuanbin Wu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes our submissions to Task 2 in SemEval 2018, i.e., Multilingual Emoji Prediction. We first investigate several traditional Natural Language Processing (NLP) features, and then design several deep learning models. For subtask 1: Emoji Prediction in English, we combine two different methods to represent tweet, i.e., supervised model using traditional features and deep learning model. For subtask 2: Emoji Prediction in Spanish, we only use deep learning model.

pdf bib
ECNU at SemEval-2018 Task 10: Evaluating Simple but Effective Features on Machine Learning Methods for Semantic Difference Detection
Yunxiao Zhou | Man Lan | Yuanbin Wu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes the system we submitted to Task 10 (Capturing Discriminative Attributes) in SemEval 2018. Given a triple (word1, word2, attribute), this task is to predict whether it exemplifies a semantic difference or not. We design and investigate several word embedding features, PMI features and WordNet features together with supervised machine learning methods to address this task. Officially released results show that our system ranks above average.

pdf bib
ECNU at SemEval-2018 Task 11: Using Deep Learning Method to Address Machine Comprehension Task
Yixuan Sheng | Man Lan | Yuanbin Wu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper describes the system we submitted to the Task 11 in SemEval 2018, i.e., Machine Comprehension using Commonsense Knowledge. Given a passage and some questions that each have two candidate answers, this task requires the participate system to select out one answer meet the meaning of original text or commonsense knowledge from the candidate answers. For this task, we use a deep learning method to obtain final predict answer by calculating relevance of choices representations and question-aware document representation.

pdf bib
ECNU at SemEval-2018 Task 12: An End-to-End Attention-based Neural Network for the Argument Reasoning Comprehension Task
Junfeng Tian | Man Lan | Yuanbin Wu
Proceedings of the 12th International Workshop on Semantic Evaluation

This paper presents our submissions to SemEval 2018 Task 12: the Argument Reasoning Comprehension Task. We investigate an end-to-end attention-based neural network to represent the two lexically close candidate warrants. On the one hand, we extract their different parts as attention vectors to obtain distinguishable representations. On the other hand, we use their surrounds (i.e., claim, reason, debate context) as another attention vectors to get contextual representations, which work as final clues to select the correct warrant. Our model achieves 60.4% accuracy and ranks 3rd among 22 participating systems.

pdf bib
Extracting Entities and Relations with Joint Minimum Risk Training
Changzhi Sun | Yuanbin Wu | Man Lan | Shiliang Sun | Wenting Wang | Kuang-Chih Lee | Kewen Wu
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

We investigate the task of joint entity relation extraction. Unlike prior efforts, we propose a new lightweight joint learning paradigm based on minimum risk training (MRT). Specifically, our algorithm optimizes a global loss function which is flexible and effective to explore interactions between the entity model and the relation model. We implement a strong and simple neural network where the MRT is executed. Experiment results on the benchmark ACE05 and NYT datasets show that our model is able to achieve state-of-the-art joint extraction performances.

2017

pdf bib
A Fast and Lightweight System for Multilingual Dependency Parsing
Tao Ji | Yuanbin Wu | Man Lan
Proceedings of the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies

We present a multilingual dependency parser with a bidirectional-LSTM (BiLSTM) feature extractor and a multi-layer perceptron (MLP) classifier. We trained our transition-based projective parser in UD version 2.0 datasets without any additional data. The parser is fast, lightweight and effective on big treebanks. In the CoNLL 2017 Shared Task: Multilingual Parsing from Raw Text to Universal Dependencies, the official results show that the macro-averaged LAS F1 score of our system Mengest is 61.33%.

pdf bib
Multi-task Attention-based Neural Networks for Implicit Discourse Relationship Representation and Identification
Man Lan | Jianxiang Wang | Yuanbin Wu | Zheng-Yu Niu | Haifeng Wang
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We present a novel multi-task attention based neural network model to address implicit discourse relationship representation and identification through two types of representation learning, an attention based neural network for learning discourse relationship representation with two arguments and a multi-task framework for learning knowledge from annotated and unannotated corpora. The extensive experiments have been performed on two benchmark corpora (i.e., PDTB and CoNLL-2016 datasets). Experimental results show that our proposed model outperforms the state-of-the-art systems on benchmark corpora.

pdf bib
Large-scale Opinion Relation Extraction with Distantly Supervised Neural Network
Changzhi Sun | Yuanbin Wu | Man Lan | Shiliang Sun | Qi Zhang
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We investigate the task of open domain opinion relation extraction. Different from works on manually labeled corpus, we propose an efficient distantly supervised framework based on pattern matching and neural network classifiers. The patterns are designed to automatically generate training data, and the deep learning model is design to capture various lexical and syntactic features. The result algorithm is fast and scalable on large-scale corpus. We test the system on the Amazon online review dataset. The result shows that our model is able to achieve promising performances without any human annotations.

pdf bib
ECNU at SemEval-2017 Task 1: Leverage Kernel-based Traditional NLP features and Neural Networks to Build a Universal Model for Multilingual and Cross-lingual Semantic Textual Similarity
Junfeng Tian | Zhiheng Zhou | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

To address semantic similarity on multilingual and cross-lingual sentences, we firstly translate other foreign languages into English, and then feed our monolingual English system with various interactive features. Our system is further supported by combining with deep learning semantic similarity and our best run achieves the mean Pearson correlation 73.16% in primary track.

pdf bib
ECNU at SemEval-2017 Task 3: Using Traditional and Deep Learning Methods to Address Community Question Answering Task
Guoshun Wu | Yixuan Sheng | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes the systems we submitted to the task 3 (Community Question Answering) in SemEval 2017 which contains three subtasks on English corpora, i.e., subtask A: Question-Comment Similarity, subtask B: Question-Question Similarity, and subtask C: Question-External Comment Similarity. For subtask A, we combined two different methods to represent question-comment pair, i.e., supervised model using traditional features and Convolutional Neural Network. For subtask B, we utilized the information of snippets returned from Search Engine with question subject as query. For subtask C, we ranked the comments by multiplying the probability of the pair related question comment being Good by the reciprocal rank of the related question.

pdf bib
ECNU at SemEval-2017 Task 7: Using Supervised and Unsupervised Methods to Detect and Locate English Puns
Yuhuan Xiu | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our submissions to task 7 in SemEval 2017, i.e., Detection and Interpretation of English Puns. We participated in the first two subtasks, which are to detect and locate English puns respectively. For subtask 1, we presented a supervised system to determine whether or not a sentence contains a pun using similarity features calculated on sense vectors or cluster center vectors. For subtask 2, we established an unsupervised system to locate the pun by scoring each word in the sentence and we assumed that the word with the smallest score is the pun.

pdf bib
ECNU at SemEval-2017 Task 8: Rumour Evaluation Using Effective Features and Supervised Ensemble Models
Feixiang Wang | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our submissions to task 8 in SemEval 2017, i.e., Determining rumour veracity and support for rumours. Given a rumoured tweet and a lot of reply tweets, the subtask A is to label whether these tweets are support, deny, query or comment, and the subtask B aims to predict the veracity (i.e., true, false, and unverified) with a confidence (in range of 0-1) of the given rumoured tweet. For both subtasks, we adopted supervised machine learning methods, incorporating rich features. Since training data is imbalanced, we specifically designed a two-step classifier to address subtask A .

pdf bib
ECNU at SemEval-2017 Task 4: Evaluating Effective Features on Machine Learning Methods for Twitter Message Polarity Classification
Yunxiao Zhou | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper reports our submission to subtask A of task 4 (Sentiment Analysis in Twitter, SAT) in SemEval 2017, i.e., Message Polarity Classification. We investigated several traditional Natural Language Processing (NLP) features, domain specific features and word embedding features together with supervised machine learning methods to address this task. Officially released results showed that our system ranked above average.

pdf bib
ECNU at SemEval-2017 Task 5: An Ensemble of Regression Algorithms with Effective Features for Fine-Grained Sentiment Analysis in Financial Domain
Mengxiao Jiang | Man Lan | Yuanbin Wu
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our systems submitted to the Fine-Grained Sentiment Analysis on Financial Microblogs and News task (i.e., Task 5) in SemEval-2017. This task includes two subtasks in microblogs and news headline domain respectively. To settle this problem, we extract four types of effective features, including linguistic features, sentiment lexicon features, domain-specific features and word embedding features. Then we employ these features to construct models by using ensemble regression algorithms. Our submissions rank 1st and rank 5th in subtask 1 and subtask 2 respectively.

2013

pdf bib
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task
Hwee Tou Ng | Joel Tetreault | Siew Mei Wu | Yuanbin Wu | Christian Hadiwinoto
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

pdf bib
The CoNLL-2013 Shared Task on Grammatical Error Correction
Hwee Tou Ng | Siew Mei Wu | Yuanbin Wu | Christian Hadiwinoto | Joel Tetreault
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task

pdf bib
Grammatical Error Correction Using Integer Linear Programming
Yuanbin Wu | Hwee Tou Ng
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

2011

pdf bib
Structural Opinion Mining for Graph-based Sentiment Representation
Yuanbin Wu | Qi Zhang | Xuanjing Huang | Lide Wu
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2009

pdf bib
Phrase Dependency Parsing for Opinion Mining
Yuanbin Wu | Qi Zhang | Xuanjing Huang | Lide Wu
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing