Measuring the ‘ I don’t know’ Problem through the Lens of G ricean Quantity (2021)

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.

## introduction neural generative dialog models have a tendency to produce generic, safe responses, such as 'i don't know' @xcite . 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 @xcite @xcite @xcite . 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 @xcite @xcite 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 @xcite , 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 @xcite @xcite . we discuss each of the categories of maxims and the ways a chatbot might violate them in 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 @xcite , 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 be clear, brief, and orderly. if there are multiple references we would recommend comparing the lowest likelihood reference for ruq score, since all valid references should be better than i don't know. we note that ruq captures some types of quantity violations, but not all violations of this maxim. ## data following khayrallah and sedoc (2020), we train and evaluate on dailydialog @xcite , foot_0 which consists of ∼ 80,000 turns of english-learners practicing 'daily dialogues' in various contexts, e.g., chatting about vacation or food. we also use entropy-based data filtering @xcite , which filters out high entropy utterances 2 with the goal of removing generic ones. we use the recommended filtering threshold of 1.0 and 'identity' clustering. we filter based on their 'source', 'target', and 'both' settings. we consider 'target' as the baseline, as they find it works best. we denote models trained on dailydialog as dd and models trained on csáky et al.'s entropy filtered version as ef. ## models following @xcite , we train transformer @xcite chatbots in fairseq using parameters from the flores benchmark for low-resource mt @xcite ): foot_6 5-layer encoder and decoder, 512 dimensional embeddings, and 2 encoder and decoder attention heads. the default regularization parameters are 0.2 label smoothing @xcite , 0.4 dropout, and 0.2 attention & relu dropout. training data base best dailydialog 12.7 17.8 entropy-filtered 13.2 17.2 ## discussion the relative ruq rankings of the four systems we consider in this work are the same as the relative rankings by multi-reference meteor, and dd-best (the single best model according to mulit-reference meteor) is also the one with the highest ruq score. among all models in the hyperparameter sweep, ruq is correlated with meteor with spearman's ρ of 0.9 but this drops to 0.6 when considering only the top 20 systems, demonstrating that ruq and meteor do not capture the same phenomenon. we note that ruq on the training data does not require a particular (multi-reference) test set like most automatic evaluation metrics. ruq simply diagnoses how well the model learned the training data compared to a generic response. the model's relative preference of idk over the (presumably) better reference response is not only a quantity violation, but is also indicative of a fundamental problem with the models themselves, and should be fixed before decoding time (either by correcting the data, or by correcting the model). @xcite argue that the idk problem is due to the one-to-many/many-to-one nature of dialog training data-if a single response applies to many different responses, it will become the canonical response. therefore their entropy filtering method removes one-to-many/many-toone pairs, by removing high entropy responses. while this data filtering reduces the problem, we found that the baseline model trained on the entropy filtered data (ef-base) still preferred idk over the reference the majority of the time, suggesting opportunities for future research on the idk problem. jwalapuram (2017 ) propose a gricean dialog evaluation where humans rate performance on a likert scale for each category. qwaider et al. (2017) consider the quantity, relation, and manner maxims for ranking community question answers. they use other nlp tools to evaluate if the response has key elements or named entities (quantity/relation), has high semantic similarity (relation), and includes/excludes positive/negative polarity terms (manner). chatbot evaluation automatic evaluations for dialog typically measure lexical or semantic similarity between a produced response and a reference, under the assumption that the reference is a good response and responses similar to it will be good as well. since there are often multiple valid responses to a prompt, this can be extended to multiple references too. in contrast, in our work we compare a model's score of a reference to a model's score of a generic response for directed analysis. huse @xcite uses the model score combined with human judgments to evaluate diversity and quality, classifying a response as human-or machine-generated. our work does not require human judgments, and compares the model score of a generic response to the reference response. mehri and eskenazi (2020a) also use scoring from a model. whereas that work is using an external model, we propose an intrinsic diagnostic for a particular phenomenon. each serves a different purpose, and an advantage of our method is our analysis does not require an external model, which might not be available in all languages and for all types of text. mitigating the idk problem a variety of approaches have been proposed to mitigate the idk problem. these include active postprocessing methods such as mmi @xcite , as well as training data filtration @xcite , reinforcement learning @xcite and unlikelihood training @xcite . in our work, we propose an intrinsic model diagnostic to analyze the problem. mmi maximum mutual information was proposed as a 'diversity-promoting objective function' for dialog @xcite . mmi-bidi encourages the prompt to be predictable from the response, by using a reverse direction model. we argue this was not diversity broadly speaking, but actually tackling a relevancy problem, since it is scoring how predictable the prompt is from the response. li et al. demonstrate mmi improves performance, though recent work found that it does not always @xcite . @xcite found that copying was overrepresented in the output of rnn nmt. using an analysis that inspired ruq plots they compare the score of the beamsearch output to that of the copied source. they also consider the probability at each position in the output, and find the model is unlikely to start copying; however, after starting to copy continuing to copy has high probability. we find idk has a relatively high score from the start, though for some models the gap widens towards the end of the sentence. ## conclusion we reframe the idk problem as a violation of the gricean maxim of quantity, and introduce a new measure-relative utterance quantity (ruq)which allows researchers to diagnose if their model is violating this particular conversational principle, and analyze methods that aim to address it. we aim to encourage further discussion and research drawing on linguistic principles about discourse and pragmatics for analysis of dialog models. for inclusion were over 500 approved hits, an approval rate over 98%, and location set to us. each hit was paid $0.15 with an overlap of 4 annotators per hit. 19 a screenshot of the hit is in figure 2 .