Can You Unpack That? Learning to Rewrite Questions-in-Context

Elgohary, Ahmed; Peskov, Denis; Boyd-Graber, Jordan

doi:10.18653/v1/d19-1605

Cited by 125 publications

(193 citation statements)

References 20 publications

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“…Question Rewriting is a novel, well-defined task which we propose for differentiating distinct answers. To the best of our knowledge, it has not been studied for resolving ambiguity; we are only aware of Elgohary et al (2019) which use question rewriting to convert conversational questions into self-contained questions.…”

Section: Related Workmentioning

confidence: 99%

AmbigQA: Answering Ambiguous Open-domain Questions

Min¹,

Michael²,

Hajishirzi³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Ambiguity is inherent to open-domain question answering; especially when exploring new topics, it can be difficult to ask questions that have a single, unambiguous answer. In this paper, we introduce AMBIGQA, a new open-domain question answering task which involves finding every plausible answer, and then rewriting the question for each one to resolve the ambiguity. To study this task, we construct AMBIGNQ, a dataset covering 14,042 questions from NQ-OPEN, an existing opendomain QA benchmark. We find that over half of the questions in NQ-OPEN are ambiguous, with diverse sources of ambiguity such as event and entity references. We also present strong baseline models for AMBIGQA which we show benefit from weakly supervised learning that incorporates NQ-OPEN, strongly suggesting our new task and data will support significant future research effort. Our data and baselines are available at https://nlp.cs. washington.edu/ambigqa.

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Section: Related Workmentioning

confidence: 99%

AmbigQA: Answering Ambiguous Open-domain Questions

Min¹,

Michael²,

Hajishirzi³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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“…2019a; Pan et al, 2019;Elgohary et al, 2019;. IUR aims to rewrite an incomplete utterance into an utterance which is semantically equivalent but self-contained to be understood without context.…”

Section: Turnmentioning

confidence: 99%

“…Recently, it has raised a large attention in several domains. In question answering, previous works include non-sentential utterance resolution using the sequence to sequence based architecture (Kumar and Joshi, 2016), incomplete follow-up question resolution via a retrieval sequence to sequence model (Kumar and Joshi, 2017) and sequence to sequence model with a copy mechanism (Elgohary et al, 2019;Quan et al, 2019). In conversational semantic parsing, Liu et al (2019b) proposed a novel approach which considers the structures of questions, while Figure 1: The illustration of the word-level edit matrix applied in our formulation.…”

Section: Related Workmentioning

confidence: 99%

Incomplete Utterance Rewriting as Semantic Segmentation

Liu¹,

Chen²,

Lou³

et al. 2020

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Recent years the task of incomplete utterance rewriting has raised a large attention. Previous works usually shape it as a machine translation task and employ sequence to sequence based architecture with copy mechanism. In this paper, we present a novel and extensive approach, which formulates it as a semantic segmentation task. Instead of generating from scratch, such a formulation introduces edit operations and shapes the problem as prediction of a word-level edit matrix. Benefiting from being able to capture both local and global information, our approach achieves state-ofthe-art performance on several public datasets. Furthermore, our approach is four times faster than the standard approach in inference.

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“…For instance, Kotov and Zhai (2010) and Sajjad et al (2012) use question templates to generate a list of clarification questions. Elgohary et al (2019) rewrite questions using the dialogue context. invoke graph edit distance for query refinement.…”

Section: Related Workmentioning

confidence: 99%

Interactive Question Clarification in Dialogue via Reinforcement Learning

Hu¹,

Wen²,

Wang

et al. 2020

Proceedings of the 28th International Conference on Computational Linguistics: Industry Track

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Coping with ambiguous questions has been a perennial problem in real-world dialogue systems. Although clarification by asking questions is a common form of human interaction, it is hard to define appropriate questions to elicit more specific intents from a user. In this work, we propose a reinforcement model to clarify ambiguous questions by suggesting refinements of the original query. We first formulate a collection partitioning problem to select a set of labels enabling us to distinguish potential unambiguous intents. We list the chosen labels as intent phrases to the user for further confirmation. The selected label along with the original user query then serves as a refined query, for which a suitable response can more easily be identified. The model is trained using reinforcement learning with a deep policy network. We evaluate our model based on real-world user clicks and demonstrate significant improvements across several different experiments.

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Can You Unpack That? Learning to Rewrite Questions-in-Context

Cited by 125 publications

References 20 publications

AmbigQA: Answering Ambiguous Open-domain Questions

AmbigQA: Answering Ambiguous Open-domain Questions

Incomplete Utterance Rewriting as Semantic Segmentation

Interactive Question Clarification in Dialogue via Reinforcement Learning

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