Papers by Zewei Chu
Mining Knowledge for Natural Language Inference from Wikipedia Categories (2020.findings-emnlp)
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| Challenge: | Accurate lexical entailment (LE) and natural language inference (NLI) tasks require expensive annotations. |
| Approach: | They propose to pretrain Wikipedia categories for lexical entailment and natural language inference by pretraining them on WikiNLI and transferring them to other knowledge bases. |
| Outcome: | The proposed model can improve strong baselines such as BERT and RoBERTa by pretraining on WikiNLI and transferring the models on downstream tasks. |
Evaluation Benchmarks and Learning Criteria for Discourse-Aware Sentence Representations (D19-1)
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| Challenge: | Prior work on pretrained sentence embeddings and benchmarks focused on the capabilities of stand-alone sentences. |
| Approach: | They propose a test suite of tasks to evaluate whether sentence representations include broader context information. |
| Outcome: | The proposed training objectives help to encode different aspects of information in document structures. |
Unsupervised Label Refinement Improves Dataless Text Classification (2021.findings-acl)
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| Challenge: | Prior work has shown that dataless classifiers can classify text into categories without using annotated training data. |
| Approach: | They propose a clustering-based approach that assigns a score to any document paired with a label description. |
| Outcome: | The proposed method improves classification performance across multiple datasets and makes it more robust to label descriptions. |
SummScreen: A Dataset for Abstractive Screenplay Summarization (2022.acl-long)
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| Challenge: | Existing summarization datasets are constructed from various domains, such as news, and we characterize them using two entity-centric metrics. |
| Approach: | They propose to use a summarization dataset to evaluate TV series transcripts and recaps . they propose to employ two entity-centric metrics to evaluate the dataset . |
| Outcome: | The proposed model outperforms the existing model and its oracle counterparts in character overlap and accuracy. |
PoMo: Generating Entity-Specific Post-Modifiers in Context (N19-1)
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Jun Seok Kang, Robert Logan, Zewei Chu, Yang Chen, Dheeru Dua, Kevin Gimpel, Sameer Singh, Niranjan Balasubramanian
| Challenge: | Using crowdsourcing, we show that contextual relevance is necessary for accurate post-modifier generation. |
| Approach: | They introduce entity post-modifier generation as an instance of a collaborative writing task . they build a post- modifier dataset from news articles that provides contextually relevant information about the target entity. |
| Outcome: | The proposed system can generate a post-modifier phrase that provides contextually relevant information about the target entity. |
EntEval: A Holistic Evaluation Benchmark for Entity Representations (D19-1)
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| Challenge: | EntEval is a test suite of tasks that require nontrivial understanding of entities. |
| Approach: | They propose to encode the mention context or the Wikipedia hyperlink annotations to learn better entity representations. |
| Outcome: | The proposed model improves strong baselines on multiple EntEval tasks. |