C o HS - CQG : Context and History Selection for Conversational Question Generation (2022)

Abstract: Conversational question generation (CQG) serves as a vital task for machines to assist humans, such as interactive reading comprehension, through conversations. Compared to traditional single-turn question generation (SQG), CQG is more challenging in the sense that the generated question is required not only to be meaningful, but also to align with the provided conversation. Previous studies mainly focus on how to model the flow and alignment of the conversation, but do not thoroughly study which parts of the context and history are necessary for the model. We believe that shortening the context and history is crucial as it can help the model to optimise more on the conversational alignment property. To this end, we propose CoHS-CQG, a two-stage CQG framework, which adopts a novel CoHS module to shorten the context and history of the input. In particular, it selects the top-p sentences and history turns by calculating the relevance scores of them. Our model achieves state-of-the-art performances on CoQA in both the answer-aware and answer-unaware settings.

## introduction one of the key goals of ai is to build systems that can understand and assist humans through conversation. in conversations, asking questions is an important dialogue act that serves as an important communication skill for ai models to better interact with humans @xcite . taking it a step further, asking good questions could facilitate collecting users' intentions and feedback, starting a new topic, and enhancing the interactivity and persistence of dialogues. in nlp, this line of research is formulated as the task of conversational question generation (cqg), which aims to generate questions based on the conversation history @xcite . although question generation has been explored intensively @xcite , most existing studies focus on single-turn question generation, which aims to generate one question from a given context. however, in the scene of conversation, it poses an additional challenge of multiturn question generation, in which the model is required to generate multiple questions during the conversation, and the generated questions should be coherent and form a smooth conversation flow. despite the intensive exploration of single-turn qg, less attention has been drawn on cqg. previous work of cqg mostly focuses on solving two main challenges: coreference alignment and conversation flow. @xcite proposed cfnet to model coreference alignment and conversation flow explicitly. @xcite proposed chaincqg, a two-stage model with two modules: the answer encoder learns the representation of the context and answer in each turn, and the question genera-tion learns the representation of the conversational history and generates the next turn's question. however, most previous work makes use of all the context and conversation history indiscriminately. on the contrary, we argue that not all sentences in the context, and not all previous turns in the conversation history are necessary for the model to generate the question in the next turn, and they may even harm the generation capacity of the model. figure 1 shows such an example, where we see that only the blue parts of the context and history are necessary for generating the 16-th turn's question. to address the above concerns, we introduce cohs-cqg, a two-stage cqg model, as described in figure 2 . in the answer-aware stage, we input sentences in the context and the conversation history turns into a pretrained sentence-transformer @xcite for calculateing the relevance scores of the (sentence, history turn) pairs. context and history selection (cohs) module (section 3.1) is then employed to shorten the context and conversation history concurrently by selecting top-p {sentence, history turn} pairs of contiguous sentences in the context, and contiguous previous turns in the history. the shortened context and history are then fed into a t5-based @xcite question generation model to generate the questions. by training the model on the shortened context and history, we observe that generated questions are generally more aligned with the conversation, which reflects that the model is optimised better in the conversational alignment. our model achieves state-of-the-art results in the cqg answer-aware setting on both automatic evaluation metrics and a careful human evaluation. in the answer-unaware stage, we propose a pipeline approach (section 3.2) to leverage our model on the answer-unaware setting, which also achieves the state-of-the-art performance on human evaluation. in summary, our main contributions are: (1) cohs-cqg, a two-stage cqg framework for both answer-aware and answer-unaware settings, which adopts a novel module, cohs, to shorten the context and history before inputting them to the qg model. cohs can be plugged into any cqg model, which makes it easily reproducible, (2) new strong state-of-the-art performances on answer-aware and answer-unaware cqg, and (3) a thorough analysis and evaluation about the selection capacity of cohs. ## cohs-cqg we formulate the conversational question generation (cqg) task in two different settings: answer-aware and answer-unaware. for the answer-aware cqg, given the referential context @xmath0, c 2 , ..., c m } where c i is the ith sentence in context, the conversation history h @xmath1, a 1 ), (q 2 , a 2 ), ..., (q n-1 , a n-1 )}, where (q i , a i ) is the i-th turn question-answer pair in conversation, the target answer a n , and the rationale r n , as input d a @xmath2, h n , a n , r n }, the model then learns to generate the question q n . the rationale r n is an associated text span from the context which contains or explains the given answer a n . for the answer-unaware cqg, however, given d u @xmath3, h n }, the model learns to generate the current question q n without a n and r n . our proposed cohs-cqg framework is shown in figure 2 . the context c and conversation history h n are first fed into a sentence encoder (se) to compute the relevance scores. in the answer-aware setting, the relevance scores, together with a n and r n are input to the context and history selection (cohs) for selecting the parts of c and h n that are most relevant to the current generation turn, and they are then input to the question generation (qg) module. in the answer-unaware case, since a n is unavailable, c and h n are first fed into the answerspan extractor (ae) to extract a n , and a n is later verified by the question filtering (qf) module. ## conclusion this paper presents cohs-cqg, a two-stage framework for cqg, which adopts a cohs module to dy-namically select relevant context and conversation history for generating the question in the current turn. experimental results on coqa demonstrate that the proposed cohs-cqg achieves state-of-theart performances in both answer-aware and answerunaware settings. our extensive analysis and studies show the effectiveness of cohs in improving the cqg models. in future work, we will focus on how to select the contiguous question-worthy content from the paragraph by reasoning.