Qiwei Peng

Selected Publications

1. LREC-COLING

   HumanEval-XL: A Multilingual Code Generation Benchmark for Cross-Lingual Natural Language Generalization

   Qiwei Peng*, Yekun Chai*, and Xuhong Li

   In The 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation May 2024

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2. *SEM

   Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap

   Qiwei Peng, David Weir, and Julie Weeds

   In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023) Jul 2023

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3. COLINGOral

   Towards Structure-aware Paraphrase Identification with Phrase Alignment Using Sentence Encoders

   Qiwei Peng, David Weir, and Julie Weeds

   In Proceedings of the 29th International Conference on Computational Linguistics Oct 2022
4. ACLOral

   Predicate-Argument Based Bi-Encoder for Paraphrase Identification

   Qiwei Peng, David Weir, Julie Weeds, and Yekun Chai

   In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) May 2022

   Abs URL

   Paraphrase identification involves identifying whether a pair of sentences express the same or similar meanings. While cross-encoders have achieved high performances across several benchmarks, bi-encoders such as SBERT have been widely applied to sentence pair tasks. They exhibit substantially lower computation complexity and are better suited to symmetric tasks. In this work, we adopt a bi-encoder approach to the paraphrase identification task, and investigate the impact of explicitly incorporating predicate-argument information into SBERT through weighted aggregation. Experiments on six paraphrase identification datasets demonstrate that, with a minimal increase in parameters, the proposed model is able to outperform SBERT/SRoBERTa significantly. Further, ablation studies reveal that the predicate-argument based component plays a significant role in the performance gain.

5. RepL4NLP@ACL

   Structure-aware Sentence Encoder in Bert-Based Siamese Network

   Qiwei Peng, David Weir, and Julie Weeds

   In Proceedings of the 6th Workshop on Representation Learning for NLP (RepL4NLP-2021) Aug 2021

   Abs URL

   Recently, impressive performance on various natural language understanding tasks has been achieved by explicitly incorporating syntax and semantic information into pre-trained models, such as BERT and RoBERTa. However, this approach depends on problem-specific fine-tuning, and as widely noted, BERT-like models exhibit weak performance, and are inefficient, when applied to unsupervised similarity comparison tasks. Sentence-BERT (SBERT) has been proposed as a general-purpose sentence embedding method, suited to both similarity comparison and downstream tasks. In this work, we show that by incorporating structural information into SBERT, the resulting model outperforms SBERT and previous general sentence encoders on unsupervised semantic textual similarity (STS) datasets and transfer classification tasks.

6. Findings@ACL

   Representing Syntax and Composition with Geometric Transformations

   Lorenzo Bertolini, Julie Weeds, David Weir, and Qiwei Peng

   In Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 Aug 2021

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