Chia-Hsuan Lee


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Proceedings of the Workshop on Multilingual Information Access (MIA)
Akari Asai | Eunsol Choi | Jonathan H. Clark | Junjie Hu | Chia-Hsuan Lee | Jungo Kasai | Shayne Longpre | Ikuya Yamada | Rui Zhang
Proceedings of the Workshop on Multilingual Information Access (MIA)

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MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages
Akari Asai | Shayne Longpre | Jungo Kasai | Chia-Hsuan Lee | Rui Zhang | Junjie Hu | Ikuya Yamada | Jonathan H. Clark | Eunsol Choi
Proceedings of the Workshop on Multilingual Information Access (MIA)

We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typologically diverse languages. In this task, we adapted two large-scale cross-lingual open-retrieval QA datasets in 14 typologically diverse languages, and newly annotated open-retrieval QA data in 2 underrepresented languages: Tagalog and Tamil. Four teams submitted their systems. The best constrained system uses entity-aware contextualized representations for document retrieval, thereby achieving an average F1 score of 31.6, which is 4.1 F1 absolute higher than the challenging baseline. The best system obtains particularly significant improvements in Tamil (20.8 F1), whereas most of the other systems yield nearly zero scores. The best unconstrained system achieves 32.2 F1, outperforming our baseline by 4.5 points.

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DOCmT5: Document-Level Pretraining of Multilingual Language Models
Chia-Hsuan Lee | Aditya Siddhant | Viresh Ratnakar | Melvin Johnson
Findings of the Association for Computational Linguistics: NAACL 2022

In this paper, we introduce DOCmT5, a multilingual sequence-to-sequence language model pretrained with large-scale parallel documents. While previous approaches have focused on leveraging sentence-level parallel data, we try to build a general-purpose pretrained model that can understand and generate long documents. We propose a simple and effective pretraining objective - Document reordering Machine Translation (DrMT), in which the input documents that are shuffled and masked need to be translated. DrMT brings consistent improvements over strong baselines on a variety of document-level generation tasks, including over 12 BLEU points for seen-language pair document-level MT, over 7 BLEU points for unseen-language-pair document-level MT and over 3 ROUGE-1 points for seen-language pair cross-lingual summarization. We achieve state-of-the-art (SOTA) on WMT20 De-En and IWSLT15 Zh-En document translation tasks. We also conduct extensive analysis on various factors for document pretraining, including (1) the effects of pretraining data quality and (2) The effects of combining mono-lingual and cross-lingual pretraining. We plan to make our model checkpoints publicly available.

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In-Context Learning for Few-Shot Dialogue State Tracking
Yushi Hu | Chia-Hsuan Lee | Tianbao Xie | Tao Yu | Noah A. Smith | Mari Ostendorf
Findings of the Association for Computational Linguistics: EMNLP 2022

Collecting and annotating task-oriented dialogues is time-consuming and costly. Thus, zero and few shot learning for dialogue tasks presents an exciting opportunity. In this work, we propose an in-context (IC) learning framework for zero-shot and few-shot learning dialogue state tracking (DST), where a large pretrained language model (LM) takes a test instance and a few exemplars as input, and directly decodes the dialogue state without any parameter updates. This approach is more flexible and scalable than prior DST work when adapting to new domains and scenarios. To better leverage a tabular domain description in the LM prompt, we reformulate DST into a text-to-SQL problem. We also propose a novel approach to retrieve annotated dialogues as exemplars. Empirical results on MultiWOZ show that our method IC-DST substantially outperforms previous fine-tuned state-of-the-art models in few-shot settings. In addition, we test IC-DST in zero-shot settings, in which the model only takes a fixed task instruction as input, finding that it outperforms previous zero-shot methods by a large margin.


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Dialogue State Tracking with a Language Model using Schema-Driven Prompting
Chia-Hsuan Lee | Hao Cheng | Mari Ostendorf
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Task-oriented conversational systems often use dialogue state tracking to represent the user’s intentions, which involves filling in values of pre-defined slots. Many approaches have been proposed, often using task-specific architectures with special-purpose classifiers. Recently, good results have been obtained using more general architectures based on pretrained language models. Here, we introduce a new variation of the language modeling approach that uses schema-driven prompting to provide task-aware history encoding that is used for both categorical and non-categorical slots. We further improve performance by augmenting the prompting with schema descriptions, a naturally occurring source of in-domain knowledge. Our purely generative system achieves state-of-the-art performance on MultiWOZ 2.2 and achieves competitive performance on two other benchmarks: MultiWOZ 2.1 and M2M. The data and code will be available at

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KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
Chia-Hsuan Lee | Oleksandr Polozov | Matthew Richardson
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)

The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such as Spider and WikiSQL facilitated novel modeling techniques for text-to-SQL parsing, improving zero-shot generalization to unseen databases. In this work, we examine the challenges that still prevent these techniques from practical deployment. First, we present KaggleDBQA, a new cross-domain evaluation dataset of real Web databases, with domain-specific data types, original formatting, and unrestricted questions. Second, we re-examine the choice of evaluation tasks for text-to-SQL parsers as applied in real-life settings. Finally, we augment our in-domain evaluation task with database documentation, a naturally occurring source of implicit domain knowledge. We show that KaggleDBQA presents a challenge to state-of-the-art zero-shot parsers but a more realistic evaluation setting and creative use of associated database documentation boosts their accuracy by over 13.2%, doubling their performance.