Measuring the ‘I don’t know’ Problem through the Lens of Gricean Quantity

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5659–5670 June 6–11, 2021. ©2021 Association for Computational Linguistics 5659 Measuring the ‘I don’t know’ Problem through the Lens of Gricean Quantity Huda Khayrallah Johns Hopkins University huda@jhu.edu João Sedoc New York University jsedoc@stern.nyu.edu Abstract We consider the intrinsic evaluation of neural generative dialog models through the lens of Grice’s Maxims of Conversation (1975). Based on the maxim of Quantity (be informative), we propose Relative Utterance Quantity (RUQ) to diagnose the ‘I don’t know’ problem, in which a dialog system produces generic responses. The linguistically motivated RUQ diagnostic compares the model score of a generic response to that of the reference response. We find that for reasonable baseline models, ‘I don’t know’ is preferred over the reference the majority of the time, but this can be reduced to less than 5% with hyperparameter tuning. RUQ allows for the direct analysis of the ‘I don’t know’ problem, which has been addressed but not analyzed by prior work. 1 Introduction Neural generative dialog models have a tendency to produce generic, safe responses, such as ‘I don’t know’ (Serban et al., 2016; Li et al., 2016a). The repetition of such phrases is annoying to users, and contributes nothing to the conversation. Evaluating chatbots is an active area of research, partly due to their open-ended nature (Hashimoto et al., 2019; Sedoc et al., 2019; Li et al., 2019; Mehri and Eskenazi, 2020b; Deriu et al., 2020). To the best of our knowledge, no prior work focuses on analyzing systems for generic, safe responses, such as ‘I don’t know.’ While prior work (Li et al., 2016a,b; Csáky et al., 2019; Welleck et al., 2020) addresses the ‘I don’t know’ problem, the lack of analysis leaves it unclear if a method improves models by mitigating this problem, or another. One linguistic framework for analyzing conversations is Grice’s Cooperative Principle (1975), which consists of Maxims of Conversation that function as guidelines for effective communication. Grice considered conversations between humans, but there has also been some exploration in NLP (Bernsen et al., 1996; Harabagiu et al., 1996; Qwaider et al., 2017; Jwalapuram, 2017). We discuss each of the categories of maxims and the ways a chatbot might violate them in Table 1. We propose a novel automatic diagnostic inspired by the Gricean QUANTITY maxim. Relative Utterance Quantity checks if the model favors a generic response (such as ‘I don’t know.’) over the reference it was trained on for each prompt. We apply our diagnostic to a method designed to address this problem (Csáky et al., 2019), and find that method does mitigate it, though not by as much as a hyperparameter search. Maxim Definition Violated by... Prompt: What color is grass? QUANTITY Be informative. not answering a question (fully), or giving too much information. I don’t know. QUALITY Be truthful. lying, or saying something without evidence. Grass is purple. RELATION Be relevant. off-topic responses. I like pizza. MANNER …

Content truncated to 500 words