Question-Interlocutor Scope Realized Graph Modeling over Key Utterances for Dialogue Reading Comprehension (2023)

Abstract: We focus on dialogue reading comprehension (DRC) that extracts answers from dialogues. Compared to standard RC tasks, DRC has raised challenges because of the complex speaker information and noisy dialogue context. Essentially, the challenges come from the speaker-centric nature of dialogue utterances — an utterance is usually insufficient in its surface form, but requires to incorporate the role of its speaker and the dialogue context to fill the latent pragmatic and intention information. We propose to deal with these problems in two folds. First, we propose a new key-utterances-extracting method, which can realize more answer-contained utterances. Second, based on the extracted utterances, we then propose a Question-Interlocutor Scope Realized Graph (QuISG). QuISG involves the question and question-mentioning speaker as nodes. To realize interlocutor scopes, utterances are connected with corresponding speakers in the dialogue. Experiments on the benchmarks show that our method achieves state-of-the-art performance against previous works.

## introduction beyond the formal forms of text, dialogues are one of the most frequently used media that people communicate with others to informally deliver their emotions @xcite , opinions @xcite , and intentions @xcite . moreover, dialogue is also a crucial information carrier in literature, such as novels and movies @xcite , for people to understand the characters and plots @xcite in their reading behaviors. therefore, comprehending dialogues is a key step for machines to act like humans. q1: what joey did after his date? … … #12 #13 #14 #15 rachel green：you know what? jeoy tribbiani：what? rachel green：there were times when it was not even me scene note：chandler and joey laughs, until they look at each other then recoil in horror. q2: why do joey and chandler recoil in horror in response to rachel ? … … text baseline prediction text gold answer swer spans for independent questions from dialogues, lags behind those of formal texts like news and wikipedia articles. 2 the reason mainly comes from distinctive features of dialogues. specifically, dialogues involve informal oral utterances which are usually short and incomplete, and thus understanding them highly depends on their loosely structured dialogue context. as a high-profile spot in the conversational-related domain, dialogue context modeling is also a major scientific problem in drc. in previous works, @xcite (abbreviated as selfsuper) point out that dialogue context modeling in drc faces two challenges: complex speaker information and noisy question-unrelated context. for speaker information, selfsuper design a self-supervised task guessing who a randomly masked speaker is according to the dialogue context (e.g., masking "monica geller" of #10 in fig. 1 ). to reduce noise, another task is made to predict whether an utterance contains the answer. although decent performance can be achieved, several urging problems still exist. firstly, speaker guessing does not aware of the speaker information in questions and the interlocutor scope. as randomly masking is independent of the question, it cannot tell which speaker in the dialogue is related to the speaker mentioned in the question, e.g., joey tribbiani to joey in q1 of fig. 1 . as for the interlocutor scope, we define it as utterances said by the corresponding speaker. we point out that utterances have a speaker-centric nature: first, each utterance has target listeners. for example, in utter. #10 of fig. 1 , it requires to understand that joey is a listener, so "you had the night" is making fun of joey from monica's scope. second, an utterance reflects the message of the experience of its speaker. for example, to answer q1 in fig. 1 , it requires understanding "stayed up all night talking" is the experience appearing in joey's scope. due to ignoring the question-mentioned interlocutor and its scope, selfsuper provides a wrong answer. secondly, answer-contained utterance (denoted as key utterance by selfsuper) prediction prefers utterances similar to the question, failing to find key utterances not similar to the question. the reason is that answers are likely to appear in utterances similar to the question. for example, about 77% of questions have answers in top-5 utterances similar to the question according to simcse @xcite in the dev set of friendsqa @xcite . furthermore, the utterances extracted by the key utterance prediction have over 82% overlaps with the top-5 utterances. therefore, there are considerable key utterances have been ignored, leading to overrated attention to similar utterances, e.g., q2 in fig. 1 . in fact, many key utterances are likely to appear near question-similar utterances because contiguous utterances in local contexts tend to be on one topic relevant to the question @xcite . however, the single utterance prediction cannot realize this. to settle the aforementioned problems, so that more answer-contained utterances can be found and the answering process realizes the question and interlocutor scopes, we propose a new pipeline framework for drc. we first propose a new keyutterances-extracting method. the method slides a window through the dialogue, where contiguous utterances in the window are regarded as a unit. the prediction is made on these units. based on utter-ances in predicted units, we then propose question-interlocutor scope realized graph (quisg) modeling. quisg constructs a graph over contextualized embeddings of words. the question and speaker names mentioned in the question are explicitly present in quisg as nodes. to remind the model of interlocutor scopes, quisg connects every speaker node in the dialogue with words from the speaker's scope. we verify our model on two popular drc benchmarks. our model achieves decent performance against baselines on both benchmarks, and further experiments indicate the efficacy of our method. conversely, we focus on drc extracting answer spans from a dialogue for an independent question @xcite . for drc, @xcite propose several pretrained and downstream tasks on the utterance level. to consider the coreference of speakers and interpersonal relationships between speakers, @xcite introduce the two types of knowledge from other dialoguerelated tasks and construct a graph to model them. besides, @xcite ; @xcite model the knowledge of discourse structure of utterances in the dialogues. to model the complex speaker information and noisy dialogue context, two selfsupervised tasks, i.e., masked-speaker guessing and key utterance prediction, are utilized or enhanced by @xcite ; @xcite ; @xcite . however, existing work ignores explicitly modeling the question and speaker scopes and suffers from low key-utterance coverage. ## experimental settings datasets. following li and zhao (2021), we conduct experiments on friendsqa @xcite and molweni @xcite . as our work does not focus on unanswerable questions, we construct an answerable version of molweni (molweni-a) by removing all unanswerable questions. friendsqa is an open-domain drc dataset collected from tv series. it contains 977/122/123 (train/dev/test) dialogues and 8,535/1,010/1,065 questions. recognizing person names in questions, we find about 76%/76%/75% of questions contain person names in friendsqa. molweni is another dataset with topics on ubuntu. it contains 8,771/883/100 dialogues and 24,682/2,513/2,871 questions, in which about 14% of questions are unanswerable. dialogues in mowelni are much shorter than in friendsqa and contain no scene descriptions. speaker names in molweni are meaningless user ids (e.g., "nbx909"). furthermore, questions containing user ids in molweni, whose proportion is about 47%/49%/48%, are less than friend-sqa. in molweni-a, there are 20,873/2,346/2,560 questions. ## conclusion to cover more key utterances and make the model realize speaker information in the question and interlocutor scopes in the dialogue for drc, we propose a new pipeline method. the method firstly adapts a new key utterances extractor with contiguous utterances as a unit for prediction. based on utterances of the extracted units, a question-interlocutor scope realized graph (quisg) is constructed. quisg sets question-mentioning speakers as question speaker nodes and connects the speaker node in the dialogue with words from its scope. our proposed method achieves decent performance on related benchmarks.