Zheng Yuan


2023

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Prompt Discriminative Language Models for Domain Adaptation
Keming Lu | Peter Potash | Xihui Lin | Yuwen Sun | Zihan Qian | Zheng Yuan | Tristan Naumann | Tianxi Cai | Junwei Lu
Proceedings of the 5th Clinical Natural Language Processing Workshop

Prompt tuning offers an efficient approach to domain adaptation for pretrained language models, which predominantly focus on masked language modeling or generative objectives. However, the potential of discriminative language models in biomedical tasks remains underexplored.To bridge this gap, we develop BioDLM, a method tailored for biomedical domain adaptation of discriminative language models that incorporates prompt-based continual pretraining and prompt tuning for downstream tasks. BioDLM aims to maximize the potential of discriminative language models in low-resource scenarios by reformulating these tasks as span-level corruption detection, thereby enhancing performance on domain-specific tasks and improving the efficiency of continual pertaining. In this way, BioDLM provides a data-efficient domain adaptation method for discriminative language models, effectively enhancing performance on discriminative tasks within the biomedical domain.

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An Extended Sequence Tagging Vocabulary for Grammatical Error Correction
Stuart Mesham | Christopher Bryant | Marek Rei | Zheng Yuan
Findings of the Association for Computational Linguistics: EACL 2023

We extend a current sequence-tagging approach to Grammatical Error Correction (GEC) by introducing specialised tags for spelling correction and morphological inflection using the SymSpell and LemmInflect algorithms. Our approach improves generalisation: the proposed new tagset allows a smaller number of tags to correct a larger range of errors. Our results show a performance improvement both overall and in the targeted error categories. We further show that ensembles trained with our new tagset outperform those trained with the baseline tagset on the public BEA benchmark.

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How effective is machine translation on low-resource code-switching? A case study comparing human and automatic metrics
Li Nguyen | Christopher Bryant | Oliver Mayeux | Zheng Yuan
Findings of the Association for Computational Linguistics: ACL 2023

This paper presents an investigation into the differences between processing monolingual input and code-switching (CSW) input in the context of machine translation (MT). Specifically, we compare the performance of three MT systems (Google, mBART-50 and M2M-100-big) in terms of their ability to translate monolingual Vietnamese, a low-resource language, and Vietnamese-English CSW respectively. To our knowledge, this is the first study to systematically analyse what might happen when multilingual MT systems are exposed to CSW data using both automatic and human metrics. We find that state-of-the-art neural translation systems not only achieve higher scores on automatic metrics when processing CSW input (compared to monolingual input), but also produce translations that are consistently rated as more semantically faithful by humans. We further suggest that automatic evaluation alone is insufficient for evaluating the translation of CSW input. Our findings establish a new benchmark that offers insights into the relationship between MT and CSW.

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Evaluation Metrics in the Era of GPT-4: Reliably Evaluating Large Language Models on Sequence to Sequence Tasks
Andrea Sottana | Bin Liang | Kai Zou | Zheng Yuan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Large Language Models (LLMs) evaluation is a patchy and inconsistent landscape, and it is becoming clear that the quality of automatic evaluation metrics is not keeping up with the pace of development of generative models. We aim to improve the understanding of current models’ performance by providing a preliminary and hybrid evaluation on a range of open and closed-source generative LLMs on three NLP benchmarks: text summarisation, text simplification and grammatical error correction (GEC), using both automatic and human evaluation. We also explore the potential of the recently released GPT-4 to act as an evaluator. We find that ChatGPT consistently outperforms many other popular models according to human reviewers on the majority of metrics, while scoring much more poorly when using classic automatic evaluation metrics. We also find that human reviewers rate the gold reference as much worse than the best models’ outputs, indicating the poor quality of many popular benchmarks. Finally, we find that GPT-4 is capable of ranking models’ outputs in a way which aligns reasonably closely to human judgement despite task-specific variations, with a lower alignment in the GEC task.

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Grammatical Error Correction: A Survey of the State of the Art
Christopher Bryant | Zheng Yuan | Muhammad Reza Qorib | Hannan Cao | Hwee Tou Ng | Ted Briscoe
Computational Linguistics, Volume 49, Issue 3 - September 2023

Grammatical Error Correction (GEC) is the task of automatically detecting and correcting errors in text. The task not only includes the correction of grammatical errors, such as missing prepositions and mismatched subject–verb agreement, but also orthographic and semantic errors, such as misspellings and word choice errors, respectively. The field has seen significant progress in the last decade, motivated in part by a series of five shared tasks, which drove the development of rule-based methods, statistical classifiers, statistical machine translation, and finally neural machine translation systems, which represent the current dominant state of the art. In this survey paper, we condense the field into a single article and first outline some of the linguistic challenges of the task, introduce the most popular datasets that are available to researchers (for both English and other languages), and summarize the various methods and techniques that have been developed with a particular focus on artificial error generation. We next describe the many different approaches to evaluation as well as concerns surrounding metric reliability, especially in relation to subjective human judgments, before concluding with an overview of recent progress and suggestions for future work and remaining challenges. We hope that this survey will serve as a comprehensive resource for researchers who are new to the field or who want to be kept apprised of recent developments.

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Exploring Partial Knowledge Base Inference in Biomedical Entity Linking
Hongyi Yuan | Keming Lu | Zheng Yuan
The 22nd Workshop on Biomedical Natural Language Processing and BioNLP Shared Tasks

Biomedical entity linking (EL) consists of named entity recognition (NER) and named entity disambiguation (NED). EL models are trained on corpora labeled by a predefined KB. However, it is a common scenario that only entities within a subset of the KB are precious to stakeholders. We name this scenario partial knowledge base inference; training an EL model with one KB and inferring on the part of it without further training. In this work, we give a detailed definition and evaluation procedures for this practically valuable but significantly understudied scenario and evaluate methods from three representative EL paradigms. We construct partial KB inference benchmarks and witness a catastrophic degradation in EL performance due to dramatically precision drop. Our findings reveal these EL paradigms can not correctly handle unlinkable mentions (NIL), so they are not robust to partial KB inference. We also propose two simple-and-effective redemption methods to combat the NIL issue with little computational overhead.

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Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)
Ekaterina Kochmar | Jill Burstein | Andrea Horbach | Ronja Laarmann-Quante | Nitin Madnani | Anaïs Tack | Victoria Yaneva | Zheng Yuan | Torsten Zesch
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

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The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues
Anaïs Tack | Ekaterina Kochmar | Zheng Yuan | Serge Bibauw | Chris Piech
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

This paper describes the results of the first shared task on generation of teacher responses in educational dialogues. The goal of the task was to benchmark the ability of generative language models to act as AI teachers, replying to a student in a teacher-student dialogue. Eight teams participated in the competition hosted on CodaLab and experimented with a wide variety of state-of-the-art models, including Alpaca, Bloom, DialoGPT, DistilGPT-2, Flan-T5, GPT- 2, GPT-3, GPT-4, LLaMA, OPT-2.7B, and T5- base. Their submissions were automatically scored using BERTScore and DialogRPT metrics, and the top three among them were further manually evaluated in terms of pedagogical ability based on Tack and Piech (2022). The NAISTeacher system, which ranked first in both automated and human evaluation, generated responses with GPT-3.5 Turbo using an ensemble of prompts and DialogRPT-based ranking of responses for given dialogue contexts. Despite promising achievements of the participating teams, the results also highlight the need for evaluation metrics better suited to educational contexts.

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The ADAIO System at the BEA-2023 Shared Task: Shared Task Generating AI Teacher Responses in Educational Dialogues
Adaeze Adigwe | Zheng Yuan
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

This paper presents the ADAIO team’s system entry in the Building Educational Applications (BEA) 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues. The task aims to assess the performance of state-of-the-art generative models as AI teachers in producing suitable responses within a student-teacher dialogue. Our system comprises evaluating various baseline models using OpenAI GPT-3 and designing diverse prompts to prompt the OpenAI models for teacher response generation. After the challenge, our system achieved second place by employing a few-shot prompt-based approach with the OpenAI text-davinci-003 model. The results highlight the few-shot learning capabilities of large-language models, particularly OpenAI’s GPT-3, in the role of AI teachers.

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HyPe: Better Pre-trained Language Model Fine-tuning with Hidden Representation Perturbation
Hongyi Yuan | Zheng Yuan | Chuanqi Tan | Fei Huang | Songfang Huang
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Language models with the Transformers structure have shown great performance in natural language processing. However, there still poses problems when fine-tuning pre-trained language models on downstream tasks, such as over-fitting or representation collapse. In this work, we propose HyPe, a simple yet effective fine-tuning technique to alleviate such problems by perturbing hidden representations of Transformers layers. Unlike previous works that only add noise to inputs or parameters, we argue that the hidden representations of Transformers layers convey more diverse and meaningful language information. Therefore, making the Transformers layers more robust to hidden representation perturbations can further benefit the fine-tuning of PLMs en bloc. We conduct extensive experiments and analyses on GLUE and other natural language inference datasets. Results demonstrate that HyPe outperforms vanilla fine-tuning and enhances generalization of hidden representations from different layers. In addition, HyPe acquires negligible computational overheads, and is better than and compatible with previous state-of-the-art fine-tuning techniques.

2022

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Fusing Heterogeneous Factors with Triaffine Mechanism for Nested Named Entity Recognition
Zheng Yuan | Chuanqi Tan | Songfang Huang | Fei Huang
Findings of the Association for Computational Linguistics: ACL 2022

Nested entities are observed in many domains due to their compositionality, which cannot be easily recognized by the widely-used sequence labeling framework.A natural solution is to treat the task as a span classification problem. To learn better span representation and increase classification performance, it is crucial to effectively integrate heterogeneous factors including inside tokens, boundaries, labels, and related spans which could be contributing to nested entities recognition. To fuse these heterogeneous factors, we propose a novel triaffine mechanism including triaffine attention and scoring.Triaffine attention uses boundaries and labels as queries and uses inside tokens and related spans as keys and values for span representations.Triaffine scoring interacts with boundaries and span representations for classification. Experiments show that our proposed method outperforms previous span-based methods, achieves the state-of-the-art F1 scores on nested NER datasets GENIA and KBP2017, and shows comparable results on ACE2004 and ACE2005.

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Automatic Biomedical Term Clustering by Learning Fine-grained Term Representations
Sihang Zeng | Zheng Yuan | Sheng Yu
Proceedings of the 21st Workshop on Biomedical Language Processing

Term clustering is important in biomedical knowledge graph construction. Using similarities between terms embedding is helpful for term clustering. State-of-the-art term embeddings leverage pretrained language models to encode terms, and use synonyms and relation knowledge from knowledge graphs to guide contrastive learning. These embeddings provide close embeddings for terms belonging to the same concept. However, from our probing experiments, these embeddings are not sensitive to minor textual differences which leads to failure for biomedical term clustering. To alleviate this problem, we adjust the sampling strategy in pretraining term embeddings by providing dynamic hard positive and negative samples during contrastive learning to learn fine-grained representations which result in better biomedical term clustering. We name our proposed method as CODER++, and it has been applied in clustering biomedical concepts in the newly released Biomedical Knowledge Graph named BIOS.

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BioBART: Pretraining and Evaluation of A Biomedical Generative Language Model
Hongyi Yuan | Zheng Yuan | Ruyi Gan | Jiaxing Zhang | Yutao Xie | Sheng Yu
Proceedings of the 21st Workshop on Biomedical Language Processing

Pretrained language models have served as important backbones for natural language processing. Recently, in-domain pretraining has been shown to benefit various domain-specific downstream tasks. In the biomedical domain, natural language generation (NLG) tasks are of critical importance, while understudied. Approaching natural language understanding (NLU) tasks as NLG achieves satisfying performance in the general domain through constrained language generation or language prompting. We emphasize the lack of in-domain generative language models and the unsystematic generative downstream benchmarks in the biomedical domain, hindering the development of the research community. In this work, we introduce the generative language model BioBART that adapts BART to the biomedical domain. We collate various biomedical language generation tasks including dialogue, summarization, entity linking, and named entity recognition. BioBART pretrained on PubMed abstracts has enhanced performance compared to BART and set strong baselines on several tasks. Furthermore, we conduct ablation studies on the pretraining tasks for BioBART and find that sentence permutation has negative effects on downstream tasks.

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CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark
Ningyu Zhang | Mosha Chen | Zhen Bi | Xiaozhuan Liang | Lei Li | Xin Shang | Kangping Yin | Chuanqi Tan | Jian Xu | Fei Huang | Luo Si | Yuan Ni | Guotong Xie | Zhifang Sui | Baobao Chang | Hui Zong | Zheng Yuan | Linfeng Li | Jun Yan | Hongying Zan | Kunli Zhang | Buzhou Tang | Qingcai Chen
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Artificial Intelligence (AI), along with the recent progress in biomedical language understanding, is gradually offering great promise for medical practice. With the development of biomedical language understanding benchmarks, AI applications are widely used in the medical field. However, most benchmarks are limited to English, which makes it challenging to replicate many of the successes in English for other languages. To facilitate research in this direction, we collect real-world biomedical data and present the first Chinese Biomedical Language Understanding Evaluation (CBLUE) benchmark: a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification, and an associated online platform for model evaluation, comparison, and analysis. To establish evaluation on these tasks, we report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.

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Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding
Zheng Yuan | Chuanqi Tan | Songfang Huang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs).Existing methods usually apply label attention with code representations to match related text snippets. Unlike these works that model the label with the code hierarchy or description, we argue that the code synonyms can provide more comprehensive knowledge based on the observation that the code expressions in EMRs vary from their descriptions in ICD. By aligning codes to concepts in UMLS, we collect synonyms of every code. Then, we propose a multiple synonyms matching network to leverage synonyms for better code representation learning, and finally help the code classification. Experiments on the MIMIC-III dataset show that our proposed method outperforms previous state-of-the-art methods.

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Improving Grammatical Error Correction for Multiword Expressions
Shiva Taslimipoor | Christopher Bryant | Zheng Yuan
Proceedings of the 18th Workshop on Multiword Expressions @LREC2022

Grammatical error correction (GEC) is the task of automatically correcting errors in text. It has mainly been developed to assist language learning, but can also be applied to native text. This paper reports on preliminary work in improving GEC for multiword expression (MWE) error correction. We propose two systems which incorporate MWE information in two different ways: one is a multi-encoder decoder system which encodes MWE tags in a second encoder, and the other is a BART pre-trained transformer-based system that encodes MWE representations using special tokens. We show improvements in correcting specific types of verbal MWEs based on a modified version of a standard GEC evaluation approach.

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Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
Ekaterina Kochmar | Jill Burstein | Andrea Horbach | Ronja Laarmann-Quante | Nitin Madnani | Anaïs Tack | Victoria Yaneva | Zheng Yuan | Torsten Zesch
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

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Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
Hongyi Yuan | Zheng Yuan | Sheng Yu
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Entities lie in the heart of biomedical natural language understanding, and the biomedical entity linking (EL) task remains challenging due to the fine-grained and diversiform concept names. Generative methods achieve remarkable performances in general domain EL with less memory usage while requiring expensive pre-training. Previous biomedical EL methods leverage synonyms from knowledge bases (KB) which is not trivial to inject into a generative method. In this work, we use a generative approach to model biomedical EL and propose to inject synonyms knowledge in it. We propose KB-guided pre-training by constructing synthetic samples with synonyms and definitions from KB and require the model to recover concept names. We also propose synonyms-aware fine-tuning to select concept names for training, and propose decoder prompt and multi-synonyms constrained prefix tree for inference. Our method achieves state-of-the-art results on several biomedical EL tasks without candidate selection which displays the effectiveness of proposed pre-training and fine-tuning strategies. The source code is available at https://github.com/Yuanhy1997/GenBioEL.

2021

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Document-level grammatical error correction
Zheng Yuan | Christopher Bryant
Proceedings of the 16th Workshop on Innovative Use of NLP for Building Educational Applications

Document-level context can provide valuable information in grammatical error correction (GEC), which is crucial for correcting certain errors and resolving inconsistencies. In this paper, we investigate context-aware approaches and propose document-level GEC systems. Additionally, we employ a three-step training strategy to benefit from both sentence-level and document-level data. Our system outperforms previous document-level and all other NMT-based single-model systems, achieving state of the art on a common test set.

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Multi-Class Grammatical Error Detection for Correction: A Tale of Two Systems
Zheng Yuan | Shiva Taslimipoor | Christopher Davis | Christopher Bryant
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In this paper, we show how a multi-class grammatical error detection (GED) system can be used to improve grammatical error correction (GEC) for English. Specifically, we first develop a new state-of-the-art binary detection system based on pre-trained ELECTRA, and then extend it to multi-class detection using different error type tagsets derived from the ERRANT framework. Output from this detection system is used as auxiliary input to fine-tune a novel encoder-decoder GEC model, and we subsequently re-rank the N-best GEC output to find the hypothesis that most agrees with the GED output. Results show that fine-tuning the GEC system using 4-class GED produces the best model, but re-ranking using 55-class GED leads to the best performance overall. This suggests that different multi-class GED systems benefit GEC in different ways. Ultimately, our system outperforms all other previous work that combines GED and GEC, and achieves a new single-model NMT-based state of the art on the BEA-test benchmark.

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Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction
Zheng Yuan | Gladys Tyen | David Strohmaier
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes our submission to the SemEval-2021 shared task on Lexical Complexity Prediction. We approached it as a regression problem and present an ensemble combining four systems, one feature-based and three neural with fine-tuning, frequency pre-training and multi-task learning, achieving Pearson scores of 0.8264 and 0.7556 on the trial and test sets respectively (sub-task 1). We further present our analysis of the results and discuss our findings.

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Cambridge at SemEval-2021 Task 2: Neural WiC-Model with Data Augmentation and Exploration of Representation
Zheng Yuan | David Strohmaier
Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)

This paper describes the system of the Cambridge team submitted to the SemEval-2021 shared task on Multilingual and Cross-lingual Word-in-Context Disambiguation. Building on top of a pre-trained masked language model, our system is first pre-trained on out-of-domain data, and then fine-tuned on in-domain data. We demonstrate the effectiveness of the proposed two-step training strategy and the benefits of data augmentation from both existing examples and new resources. We further investigate different representations and show that the addition of distance-based features is helpful in the word-in-context disambiguation task. Our system yields highly competitive results in the cross-lingual track without training on any cross-lingual data; and achieves state-of-the-art results in the multilingual track, ranking first in two languages (Arabic and Russian) and second in French out of 171 submitted systems.

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Improving Biomedical Pretrained Language Models with Knowledge
Zheng Yuan | Yijia Liu | Chuanqi Tan | Songfang Huang | Fei Huang
Proceedings of the 20th Workshop on Biomedical Language Processing

Pretrained language models have shown success in many natural language processing tasks. Many works explore to incorporate the knowledge into the language models. In the biomedical domain, experts have taken decades of effort on building large-scale knowledge bases. For example, UMLS contains millions of entities with their synonyms and defines hundreds of relations among entities. Leveraging this knowledge can benefit a variety of downstream tasks such as named entity recognition and relation extraction. To this end, we propose KeBioLM, a biomedical pretrained language model that explicitly leverages knowledge from the UMLS knowledge bases. Specifically, we extract entities from PubMed abstracts and link them to UMLS. We then train a knowledge-aware language model that firstly applies a text-only encoding layer to learn entity representation and then applies a text-entity fusion encoding to aggregate entity representation. In addition, we add two training objectives as entity detection and entity linking. Experiments on the named entity recognition and relation extraction tasks from the BLURB benchmark demonstrate the effectiveness of our approach. Further analysis on a collected probing dataset shows that our model has better ability to model medical knowledge.

2019

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Neural and FST-based approaches to grammatical error correction
Zheng Yuan | Felix Stahlberg | Marek Rei | Bill Byrne | Helen Yannakoudakis
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper, we describe our submission to the BEA 2019 shared task on grammatical error correction. We present a system pipeline that utilises both error detection and correction models. The input text is first corrected by two complementary neural machine translation systems: one using convolutional networks and multi-task learning, and another using a neural Transformer-based system. Training is performed on publicly available data, along with artificial examples generated through back-translation. The n-best lists of these two machine translation systems are then combined and scored using a finite state transducer (FST). Finally, an unsupervised re-ranking system is applied to the n-best output of the FST. The re-ranker uses a number of error detection features to re-rank the FST n-best list and identify the final 1-best correction hypothesis. Our system achieves 66.75% F 0.5 on error correction (ranking 4th), and 82.52% F 0.5 on token-level error detection (ranking 2nd) in the restricted track of the shared task.

2018

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Neural sequence modelling for learner error prediction
Zheng Yuan
Proceedings of the Thirteenth Workshop on Innovative Use of NLP for Building Educational Applications

This paper describes our use of two recurrent neural network sequence models: sequence labelling and sequence-to-sequence models, for the prediction of future learner errors in our submission to the 2018 Duolingo Shared Task on Second Language Acquisition Modeling (SLAM). We show that these two models capture complementary information as combining them improves performance. Furthermore, the same network architecture and group of features can be used directly to build competitive prediction models in all three language tracks, demonstrating that our approach generalises well across languages.

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Construction of the Literature Graph in Semantic Scholar
Waleed Ammar | Dirk Groeneveld | Chandra Bhagavatula | Iz Beltagy | Miles Crawford | Doug Downey | Jason Dunkelberger | Ahmed Elgohary | Sergey Feldman | Vu Ha | Rodney Kinney | Sebastian Kohlmeier | Kyle Lo | Tyler Murray | Hsu-Han Ooi | Matthew Peters | Joanna Power | Sam Skjonsberg | Lucy Lu Wang | Chris Wilhelm | Zheng Yuan | Madeleine van Zuylen | Oren Etzioni
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 3 (Industry Papers)

We describe a deployed scalable system for organizing published scientific literature into a heterogeneous graph to facilitate algorithmic manipulation and discovery. The resulting literature graph consists of more than 280M nodes, representing papers, authors, entities and various interactions between them (e.g., authorships, citations, entity mentions). We reduce literature graph construction into familiar NLP tasks (e.g., entity extraction and linking), point out research challenges due to differences from standard formulations of these tasks, and report empirical results for each task. The methods described in this paper are used to enable semantic features in www.semanticscholar.org.

2017

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Artificial Error Generation with Machine Translation and Syntactic Patterns
Marek Rei | Mariano Felice | Zheng Yuan | Ted Briscoe
Proceedings of the 12th Workshop on Innovative Use of NLP for Building Educational Applications

Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additional resources. We propose treating error generation as a machine translation task, where grammatically correct text is translated to contain errors. In addition, we explore a system for extracting textual patterns from an annotated corpus, which can then be used to insert errors into grammatically correct sentences. Our experiments show that the inclusion of artificially generated errors significantly improves error detection accuracy on both FCE and CoNLL 2014 datasets.

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Neural Sequence-Labelling Models for Grammatical Error Correction
Helen Yannakoudakis | Marek Rei | Øistein E. Andersen | Zheng Yuan
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

We propose an approach to N-best list reranking using neural sequence-labelling models. We train a compositional model for error detection that calculates the probability of each token in a sentence being correct or incorrect, utilising the full sentence as context. Using the error detection model, we then re-rank the N best hypotheses generated by statistical machine translation systems. Our approach achieves state-of-the-art results on error correction for three different datasets, and it has the additional advantage of only using a small set of easily computed features that require no linguistic input.

2016

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Candidate re-ranking for SMT-based grammatical error correction
Zheng Yuan | Ted Briscoe | Mariano Felice
Proceedings of the 11th Workshop on Innovative Use of NLP for Building Educational Applications

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Grammatical error correction using neural machine translation
Zheng Yuan | Ted Briscoe
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2014

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Generating artificial errors for grammatical error correction
Mariano Felice | Zheng Yuan
Proceedings of the Student Research Workshop at the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Grammatical error correction using hybrid systems and type filtering
Mariano Felice | Zheng Yuan | Øistein E. Andersen | Helen Yannakoudakis | Ekaterina Kochmar
Proceedings of the Eighteenth Conference on Computational Natural Language Learning: Shared Task

2013

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Constrained Grammatical Error Correction using Statistical Machine Translation
Zheng Yuan | Mariano Felice
Proceedings of the Seventeenth Conference on Computational Natural Language Learning: Shared Task