An Evaluation Dataset and Strategy for Building Robust Multi-turn Response Selection Model (2021)

Abstract: Multi-turn response selection models have recently shown comparable performance to humans in several benchmark datasets. However, in the real environment, these models often have weaknesses, such as making incorrect predictions based heavily on superficial patterns without a comprehensive understanding of the context. For example, these models often give a high score to the wrong response candidate containing several keywords related to the context but using the inconsistent tense. In this study, we analyze the weaknesses of the open-domain Korean Multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. We also suggest a strategy to build a robust model in this adversarial environment.

## introduction multi-turn response selection is a task that selects the best response among given candidates for a given dialogue context. response selection models have recently shown comparable performance to humans @xcite in the several indomain/held-out benchmarks @xcite @xcite . however, in the actual service environment, these models are often found to have weaknesses. for example, the model gives the highest score to the wrong response, which has high word overlap with the context @xcite or semantically similar to the context @xcite . held-out evaluation often overestimates the realworld performance of the model @xcite , so adversarial datasets for evaluating weaknesses have been constructed for each task, such as nli @xcite , and mrc @xcite . a framework for comprehensively evaluating the general linguistic abilities of the model was also studied @xcite . several works evaluated adversarial cases for the response selection task @xcite . however, they just automatically generate adversarial responses by copying words in the context. in this study, we analyze the weaknesses of the various aspects of the open-domain korean multi-turn response selection models and construct an adversarial dataset manually. a total of 2,220 test cases are constructed, and each test case are classified by type. neural networks do not generalize well to such an adversarial setting because they tend to use superficial patterns and spurious correlation of the dataset overly, which makes models biased @xcite . thus, various debiasing methods have been studied to alleviate this phenomenon @xcite . in this study, we show that debasing method is also effective in adeversarial evaluation for multi-turn response selection task. in the retrieval-based chatbot system where response selection is used, response candidates are composed as follows. all utterances in the database are used as response candidates @xcite , or part of them filtered through search engines are used @xcite . to filter the candidates, machine learning-based embeddings or word-level similarity algorithms(e.g., bm25), which also have weaknesses in an adversarial setting, are used @xcite . therefore, almost every time a response is selected by the actual system, adversarial cases are included in the candidates. thus, robustness to adversarial cases is more important for response selection task. we also construct a real environment test set and experiment that the model robust to an adversarial case has high performance in the real environment. ## adversarial test dataset we analyze the incorrect responses in the internal service log and categorize the types of frequent errors. there are a total of seven types, and details of each type are as follows. repetition an incorrect response repeating one of the utterances in the context. negation a negation is either added to or omitted from a correct response, generating an erroneous response with reversed affirmative or negative meaning. a test set for a negation error intentionally generates a negative response by adding or removing '안' or '못', which are negative adverbs in korean (short-form negation) or '-지 않다,' '-지 못하다, ' or '-지 말다' which are negative auxiliary predicates in korean (long-form negation) in order to test whether the model understands such semantic reversal. tense a morpheme or expression marking tense is added to or removed from a correct response, generating an erroneous response in tense that is inconsistent with the given context. a test set for tense errors adds or replaces morphemes or expressions marking the future tense such as '-겠-,' or ones marking the past tense such as'-었-' to test whether the model fully understands the context disconnection triggered by such tense change. subject-object a test set for subject-object errors generates a response inconsistent with the context due to confusion of the subject and object for a certain action. in particular, since zero anaphora can be found frequently in korean sentences, incorrect responses are often made because of a failure in identifying the hidden subject of the previous context. this test set uses a subject or an object differently from the ones used in a correct response to examine whether the model fully understands the context disconnection caused by such errors. ## method suppose that dataset is @xmath0, r i , y i )} n @xmath1, where c i denotes a dialogue context, r i is a response utterance, and y @xmath2, 1} is a label. the context c @xmath3,1 , u i,2 , ..., u i,k i } consists of sequence of k i utterances. the label y @xmath4. ## conclusion we analyze the weaknesses of the open-domain korean multi-turn response selection models and publish an adversarial dataset to evaluate these weaknesses. we suggest a strategy to build a robust model to an adversarial and real environment with the experimental results. we expect that this work and dataset will help improve the response selection model.