SHARE: A Lexicon of Harmful Expressions by Spanish Speakers

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 119–126 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 119 CoQAR: Question Rewriting on CoQA Quentin Brabant, Gw´enol´e Lecorv´e, Lina M. Rojas-Barahona Orange Innovation 2 Avenue Pierre Marzin. Lannion. France. {quentin.brabant, gwenole.lecorve, linamaria.rojasbarahona}@orange.com Abstract Questions asked by humans during a conversation often contain contextual dependencies, i.e., explicit or implicit references to previous dialogue turns. These dependencies take the form of coreferences (e.g., via pronoun use) or ellipses, and can make the understanding difficult for automated systems. One way to facilitate the understanding and subsequent treatments of a question is to rewrite it into an out-of-context form, i.e., a form that can be understood without the conversational context. We propose CoQAR, a corpus containing 4.5K conversations from the Conversational Question-Answering dataset CoQA, for a total of 53K follow-up question-answer pairs. Each original question was manually annotated with at least 2 at most 3 out-of- context rewritings. CoQAR can be used in the supervised learning of three tasks: question paraphrasing, question rewriting and conversational question answering. In order to assess the quality of CoQAR’s rewritings, we conduct several experiments consisting in training and evaluating models for these three tasks. Our results support the idea that question rewriting can be used as a preprocessing step for question answering models, thereby increasing their performances. Keywords: question rewriting, conversational question answering, question paraphrasing 1. Introduction Conversational Question Answering (CQA) (Reddy et al., 2019; Choi et al., 2018; Saha et al., 2018) is a task in which a system interacts with a so-called stu- dent. The interaction takes the form of a conversation, where the student asks questions, and the system is ex- pected to provide the right answers. In this paper we focus on the case where the system searches for an- swers in a text passage, although settings relying on structured data (e.g. knowledge bases) also exist (Saha et al., 2018). Compared to non-conversational question answering (or QA for short), the system faces an ad- ditional difficulty: each question is asked in a conver- sational context that consists in previous conversation turns; implicit references to the conversational context may happen in the form of ellipses and coreferences, making the understanding of questions more difficult for the system. One way to overcome this difficulty is Question Rewriting (QR), which consists in rewriting each orig- inal (in-context) question into an out-of-context ques- tion that is understandable by itself, i.e., that can be answered without knowing the conversational context. Vakulenko et al. (2021) argue in favor of this approach by experimentally showing that adding QR as a pre- processing step of CQA models can improve their per- formances. They also claim that QR models offer sev- eral advantages, including the possibility of reuse: a same QR model can be used as a preprocessing step for several existing (conversational or non-conversational) QA models and datasets. In particular, any existing non-conversational QA model (see, e.g., (Rajpurkar et al., 2018; Usbeck et …

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