Yinglin Wang


2021

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Word Sense Disambiguation: Towards Interactive Context Exploitation from Both Word and Sense Perspectives
Ming Wang | Yinglin Wang
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)

Lately proposed Word Sense Disambiguation (WSD) systems have approached the estimated upper bound of the task on standard evaluation benchmarks. However, these systems typically implement the disambiguation of words in a document almost independently, underutilizing sense and word dependency in context. In this paper, we convert the nearly isolated decisions into interrelated ones by exposing senses in context when learning sense embeddings in a similarity-based Sense Aware Context Exploitation (SACE) architecture. Meanwhile, we enhance the context embedding learning with selected sentences from the same document, rather than utilizing only the sentence where each ambiguous word appears. Experiments on both English and multilingual WSD datasets have shown the effectiveness of our approach, surpassing previous state-of-the-art by large margins (3.7% and 1.2% respectively), especially on few-shot (14.3%) and zero-shot (35.9%) scenarios.

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Doing Good or Doing Right? Exploring the Weakness of Commonsense Causal Reasoning Models
Mingyue Han | Yinglin Wang
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

Pretrained language models (PLM) achieve surprising performance on the Choice of Plausible Alternatives (COPA) task. However, whether PLMs have truly acquired the ability of causal reasoning remains a question. In this paper, we investigate the problem of semantic similarity bias and reveal the vulnerability of current COPA models by certain attacks. Previous solutions that tackle the superficial cues of unbalanced token distribution still encounter the same problem of semantic bias, even more seriously due to the utilization of more training data. We mitigate this problem by simply adding a regularization loss and experimental results show that this solution not only improves the model’s generalization ability, but also assists the models to perform more robustly on a challenging dataset, BCOPA-CE, which has unbiased token distribution and is more difficult for models to distinguish cause and effect.

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Enhancing the Context Representation in Similarity-based Word Sense Disambiguation
Ming Wang | Jianzhang Zhang | Yinglin Wang
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

In previous similarity-based WSD systems, studies have allocated much effort on learning comprehensive sense embeddings using contextual representations and knowledge sources. However, the context embedding of an ambiguous word is learned using only the sentence where the word appears, neglecting its global context. In this paper, we investigate the contribution of both word-level and sense-level global context of an ambiguous word for disambiguation. Experiments have shown that the Context-Oriented Embedding (COE) can enhance a similarity-based system’s performance on WSD by relatively large margins, achieving state-of-the-art on all-words WSD benchmarks in knowledge-based category.

2020

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A Synset Relation-enhanced Framework with a Try-again Mechanism for Word Sense Disambiguation
Ming Wang | Yinglin Wang
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Contextual embeddings are proved to be overwhelmingly effective to the task of Word Sense Disambiguation (WSD) compared with other sense representation techniques. However, these embeddings fail to embed sense knowledge in semantic networks. In this paper, we propose a Synset Relation-Enhanced Framework (SREF) that leverages sense relations for both sense embedding enhancement and a try-again mechanism that implements WSD again, after obtaining basic sense embeddings from augmented WordNet glosses. Experiments on all-words and lexical sample datasets show that the proposed system achieves new state-of-the-art results, defeating previous knowledge-based systems by at least 5.5 F1 measure. When the system utilizes sense embeddings learned from SemCor, it outperforms all previous supervised systems with only 20% SemCor data.

2011

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Generating Aspect-oriented Multi-Document Summarization with Event-aspect model
Peng Li | Yinglin Wang | Wei Gao | Jing Jiang
Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing

2010

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Generating Templates of Entity Summaries with an Entity-Aspect Model and Pattern Mining
Peng Li | Jing Jiang | Yinglin Wang
Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics