Zixian Huang

Also published as: ZiXian Huang


2023

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An Empirical Investigation of Implicit and Explicit Knowledge-Enhanced Methods for Ad Hoc Dataset Retrieval
Weiqing Luo | Qiaosheng Chen | Zhiyang Zhang | Zixian Huang | Gong Cheng
Findings of the Association for Computational Linguistics: EMNLP 2023

Ad hoc dataset retrieval has become an important way of finding data on the Web, where the underlying problem is how to measure the relevance of a dataset to a query. State-of-the-art solutions for this task are still lexical methods, which cannot capture semantic similarity. Semantics-aware knowledge-enhanced retrieval methods, which achieved promising results on other tasks, have yet to be systematically studied on this specialized task. To fill the gap, in this paper, we present an empirical investigation of the task where we implement and evaluate, on two test collections, a set of implicit and explicit knowledge-enhancement retrieval methods in various settings. Our results reveal the unique features of the task and suggest an interpolation of different kinds of methods as the current best practice.

2022

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Clues Before Answers: Generation-Enhanced Multiple-Choice QA
Zixian Huang | Ao Wu | Jiaying Zhou | Yu Gu | Yue Zhao | Gong Cheng
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

A trending paradigm for multiple-choice question answering (MCQA) is using a text-to-text framework. By unifying data in different tasks into a single text-to-text format, it trains a generative encoder-decoder model which is both powerful and universal. However, a side effect of twisting a generation target to fit the classification nature of MCQA is the under-utilization of the decoder and the knowledge that can be decoded. To exploit the generation capability and underlying knowledge of a pre-trained encoder-decoder model, in this paper, we propose a generation-enhanced MCQA model named GenMC. It generates a clue from the question and then leverages the clue to enhance a reader for MCQA. It outperforms text-to-text models on multiple MCQA datasets.

2021

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When Retriever-Reader Meets Scenario-Based Multiple-Choice Questions
ZiXian Huang | Ao Wu | Yulin Shen | Gong Cheng | Yuzhong Qu
Findings of the Association for Computational Linguistics: EMNLP 2021

Scenario-based question answering (SQA) requires retrieving and reading paragraphs from a large corpus to answer a question which is contextualized by a long scenario description. Since a scenario contains both keyphrases for retrieval and much noise, retrieval for SQA is extremely difficult. Moreover, it can hardly be supervised due to the lack of relevance labels of paragraphs for SQA. To meet the challenge, in this paper we propose a joint retriever-reader model called JEEVES where the retriever is implicitly supervised only using QA labels via a novel word weighting mechanism. JEEVES significantly outperforms a variety of strong baselines on multiple-choice questions in three SQA datasets.

2019

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GeoSQA: A Benchmark for Scenario-based Question Answering in the Geography Domain at High School Level
Zixian Huang | Yulin Shen | Xiao Li | Yu’ang Wei | Gong Cheng | Lin Zhou | Xinyu Dai | Yuzhong Qu
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Scenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a scenario. SQA widely exists in the medical, geography, and legal domains—both in practice and in the exams. In this paper, we introduce the GeoSQA dataset. It consists of 1,981 scenarios and 4,110 multiple-choice questions in the geography domain at high school level, where diagrams (e.g., maps, charts) have been manually annotated with natural language descriptions to benefit NLP research. Benchmark results on a variety of state-of-the-art methods for question answering, textual entailment, and reading comprehension demonstrate the unique challenges presented by SQA for future research.