Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation (2020)

Abstract: Neural conversation models are known to generate appropriate but non-informative responses in general. A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document. In previous work, the external document is utilized by (1) creating a context-aware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory. In this paper, we propose to create the document memory with some anticipated responses in mind. This is achieved using a teacher-student framework. The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information. The student learns to construct a response-anticipated document memory from the first two sources, and teacher’s insight on memory creation. Empirical results show that our model outperforms the previous state-of-the-art for the CbR task.

## introduction neural conversation models have achieved promising performance in response generation. however, it is widely observed that the generated responses lack sufficient content and information @xcite . one way to address this issue is to integrate various external information into conversation models. examples of external information include document topics @xcite , commonsense knowledge graphs @xcite , and domain-specific knowledge bases @xcite . conversing by reading (cbr) @xcite , 2019) is a recently proposed scenario where external information can be ingested to conversations. in cbr, conversations take place with reference to a document. the key problem in cbr is to learn how to integrate information from the external document into response generation on demand. to exploit knowledge from documents for conversations, a conventional way is to extend the sequence-to-sequence (seq2seq) model @xcite with memory networks @xcite , which store knowledge representations accessible to their decoder @xcite . @xcite propose to encode the dialogue context as well as a set of retrieved knowledge by transformer @xcite to construct the memory. however, these methods only use sentence-level representations of the documents in the memory, which cannot pinpoint accurate tokenlevel document information. to discover token-level document information, researchers borrow models from other generation tasks, which are adept at extracting segments of sentences for given questions. @xcite explore the pointer generator network @xcite for abstractive summarization and the bidirectional attention flow model @xcite , which is a qa model to predict a span of the document to be contained in the response. @xcite follow the stochastic answer network (san) @xcite in machine reading comprehension (mrc), integrating both context and document information to form the context-aware document memory. this approach obtains the stateof-the-art performance on the cbr task. however, we should notice the difference between existing generation tasks and cbr. for summarization, qa, and mrc, they require models to extract exact answers from documents, where documents cover all requisite knowledge. meanwhile, cbr expects to output a general utterance relevant to both context and document. as the example in fig. 1 , the document refers to actor, films, fans, wealthy and the context mentions disease. document and context discuss the same person but have no topic overlap; thus we cannot pinpoint document information from the context. if we use san as in @xcite , san can hardly acquire helpful information from context-document interaction. to ingest useful knowledge for response generation, we argue that processing documents should consider not only the interaction between context and document but also the target response. as in the example, the document should attend more on fans, wealthy by considering the response. in this work, we propose a method to construct a response-anticipated memory to contain document information that is potentially more important in generating responses. particularly, we construct a teacher-student framework based on @xcite . the teacher model accesses the groundtruth response, context, and document. it learns to construct a weight matrix that contains information about the importance of tokens in the document to the response. the student model learns to mimic the weight matrix constructed by the teacher without access to the response. that is, the teacher learns to build a response-aware memory, while the student learns to build a response-anticipated memory. during inference on testing data, the student will be applied. our experiments show our model exceeds all competing methods. ## related work most neural conversation models in open domain chit-chat scenarios are based on the seq2seq model @xcite . a critical issue of these models is the safe response problem, i.e., generated responses often lack enough content and information. to address this issue, previous work encourages response diversity and informativeness by introducing new training objectives @xcite , refining beam search strategies @xcite @xcite , exploiting information from conversational contexts @xcite @xcite , or incorporating with retrieval-based conversation systems @xcite @xcite . some researchers augment information in generating responses by external resources. @xcite utilize the commonsense knowledge graph by their designed graph attention. @xcite propose a knowledge encoder to encode query-entity pairs from the knowledge base. @xcite enrich response generation with knowledge triplets. these work all uses knowledge information in structured formats. external unstructured text information has also been investigated to improve conversation models. some researchers directly build "document memory" by using distributed representations of the knowledge sentences into conversation models @xcite . @xcite make use of the transformer @xcite to encode the knowledge sentences as well as the dialogue context. @xcite design a knowledge selector to construct the document memory on selective knowledge information. as stated in the introduction, some other researchers borrow models from other generation tasks, including abstractive summarization models @xcite , qa models @xcite and mrc models @xcite . especially, @xcite get the state-of-the-art performance. however, they all construct the document memory relying on connections between context and document without consideration of the response. if context or document contains a lot of noise tokens irrelevant to the response, which is indeed the case in cbr, the constructed memory may be misled by these noise information (as the case in fig. 1 ). therefore, we propose to involve the consideration of responses in the memory construction, which can benefit generating a more desired response. ## methodology in this section, we will first give an overall description of the proposed teacher-student architecture for cbr, then briefly describe the base model. the detailed teacher model and student model are presented in sec 3.3 and 3.4. lastly, we summarize the training updates of the two models in sec 3.5. ## experimental results and analysis in this part, we first show the performance of all methods in sec 5.1. then, we validate the effectiveness of response anticipation on cbr in sec 5.2 by comparing the top similar tokens with the response using their representations in the memory. we also compare more variants of our model in sec 5.3, including the token importance versus pairwise importance, and each method with continuous weights versus their variants with binary weights. at last, we conduct a case study in sec 5.4. ## conclusion focusing on the cbr task, we propose a novel response-anticipated document memory to exploit and memorize the document information that is important in response generation. we construct the response-anticipated memory by a teacher-student framework. the teacher accesses the response and learns a response-aware weight matrix; the student learns to estimate the weight matrix in the teacher model and construct the response-anticipated document memory. we verify our model on both automatic and human evaluations and experimental results show our model obtains the state-of-the-art performance on the cbr task.