Weiwen Xu


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Dynamic Semantic Graph Construction and Reasoning for Explainable Multi-hop Science Question Answering
Weiwen Xu | Huihui Zhang | Deng Cai | Wai Lam
Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

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Exploiting Reasoning Chains for Multi-hop Science Question Answering
Weiwen Xu | Yang Deng | Huihui Zhang | Deng Cai | Wai Lam
Findings of the Association for Computational Linguistics: EMNLP 2021

We propose a novel Chain Guided Retriever-reader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering. Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or human-annotated entity mentions. Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts. A Chain-aware loss, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains. Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.

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Addressing the Vulnerability of NMT in Input Perturbations
Weiwen Xu | Ai Ti Aw | Yang Ding | Kui Wu | Shafiq Joty
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Papers

Neural Machine Translation (NMT) has achieved significant breakthrough in performance but is known to suffer vulnerability to input perturbations. As real input noise is difficult to predict during training, robustness is a big issue for system deployment. In this paper, we improve the robustness of NMT models by reducing the effect of noisy words through a Context-Enhanced Reconstruction (CER) approach. CER trains the model to resist noise in two steps: (1) perturbation step that breaks the naturalness of input sequence with made-up words; (2) reconstruction step that defends the noise propagation by generating better and more robust contextual representation. Experimental results on Chinese-English (ZH-EN) and French-English (FR-EN) translation tasks demonstrate robustness improvement on both news and social media text. Further fine-tuning experiments on social media text show our approach can converge at a higher position and provide a better adaptation.


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Revisit Automatic Error Detection for Wrong and Missing Translation – A Supervised Approach
Wenqiang Lei | Weiwen Xu | Ai Ti Aw | Yuanxin Xiang | Tat Seng Chua
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

While achieving great fluency, current machine translation (MT) techniques are bottle-necked by adequacy issues. To have a closer study of these issues and accelerate model development, we propose automatic detecting adequacy errors in MT hypothesis for MT model evaluation. To do that, we annotate missing and wrong translations, the two most prevalent issues for current neural machine translation model, in 15000 Chinese-English translation pairs. We build a supervised alignment model for translation error detection (AlignDet) based on a simple Alignment Triangle strategy to set the benchmark for automatic error detection task. We also discuss the difficulties of this task and the benefits of this task for existing evaluation metrics.