A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations (2021)

Abstract: Recently, many studies are emerging towards building a retrieval-based dialogue system that is able to effectively leverage background knowledge (e.g., documents) when conversing with humans. However, it is non-trivial to collect large-scale dialogues that are naturally grounded on the background documents, which hinders the effective and adequate training of knowledge selection and response matching. To overcome the challenge, we consider decomposing the training of the knowledge-grounded response selection into three tasks including: 1) query-passage matching task; 2) query-dialogue history matching task; 3) multi-turn response matching task, and joint learning all these tasks in a unified pre-trained language model. The former two tasks could help the model in knowledge selection and comprehension, while the last task is designed for matching the proper response with the given query and background knowledge (dialogue history). By this means, the model can be learned to select relevant knowledge and distinguish proper response, with the help of ad-hoc retrieval corpora and a large number of ungrounded multi-turn dialogues. Experimental results on two benchmarks of knowledge-grounded response selection indicate that our model can achieve comparable performance with several existing methods that rely on crowd-sourced data for training.

## introduction along with the very recent prosperity of artificial intelligence empowered conversation systems in the spotlight, many studies have been focused on building human-computer dialogue systems @xcite with either retrievalbased methods @xcite @xcite or generation-based methods @xcite @xcite , which both predict the response with only the given context. in fact, unlike a person who may associate the conversation with the background knowledge in his or her mind, the machine can only capture limited information from the query message itself. as a result, it is difficult for a machine to properly comprehend the query, and to predict a proper response to make it more engaging. to bridge the gap of the knowledge between the human and the machine, researchers have begun to simulating this motivation by grounding dialogue agents with background knowledge @xcite @xcite , and lots of impressive results have been obtained. in this paper, we consider the response selection problem in knowledge-grounded conversion and specify the background knowledge as unstructured documents that are common sources in practice. the task is that given a conversation context and a set of knowledge entries, one is required 1): to select proper knowledge and grasp a good comprehension of the selected document materials (knowledge selection); 2): to distinguish the true response from a candidate pool that is relevant and consistent with both the conversation context and the background documents (knowledge matching). while there exists a number of knowledge documents on the web, it is non-trivial to collect large-scale dialogues that are naturally grounded on the documents for training a neural response selection model, which hinders the effective and adequate training of knowledge selection and response matching. although some benchmarks built upon crowd-sourcing have been released by recent works @xcite , the relatively small training size makes it hard for the dialogue models to generalize on other domains or topics @xcite . thus, in this work, we focus on a more challenging and practical scenario, learning a knowledge-grounded conversation agent without any knowledge-grounded dialogue data, which is known as zero-resource settings. since knowledge-grounded dialogues are unavailable in training, it raises greater challenges for learning the grounded response selection model. fortunately, there exists a large number of unstructured knowledge (e.g., web pages or wiki articles), passage search datasets (e.g., query-passage pairs coming from ad-hoc retrieval tasks) (khattab and zaharia, 2020) and multi-turn dialogues (e.g., context-response pairs collected from reddit) @xcite , which might be beneficial to the learning of knowledge comprehension, knowledge selection and response prediction respectively. besides, in multi-turn dialogues, the background knowledge and conversation history (excluding the latest query) are symmetric in terms of the information they convey, and we assume that the dialogue history can be regarded as another format of background knowledge for response prediction. based on the above intuition, in this paper, we consider decomposing the training of the grounded response selection task into several sub-tasks, and joint learning all those tasks in a unified model. to take advantage of the recent breakthrough on pretraining for natural language tasks, we build the grounded response matching models on the basis of a pre-trained language model (plms) @xcite , which are trained with large-scale unstructured documents from the web. on this basis, we further train the plms with query-passage matching task, query-dialogue history matching task, and multi-turn response matching task jointly. the former two tasks could help the model not only in knowledge selection but also in knowledge (and dialogue history) comprehension, while the last task is designed for matching the proper response with the given query and background knowledge (dialogue history). by this means, the model can be learned to select relevant knowledge and distinguish proper responses, with the help of a large number of ungrounded dialogues and ad-hoc retrieval corpora. during the testing stage, we first utilize the trained model to select proper knowledge, and then feed the query, dialogue history, selected knowledge, and the response candidate into our model to calculate the final matching degree. particularly, we design two strategies to compute the final matching score. in the first strategy, we directly concatenate the selected knowledge and dialogue history as a long sequence of background knowledge and feed into the model. in the second strategy, we first compute the matching degree between each queryknowledge and the response candidates, and then integrate all matching scores. we conduct experiments with benchmarks of knowledge-grounded dialogue that are constructed by crowd-sourcing, such as the wizard-of-wikipedia corpus @xcite and the cmu dog corpus @xcite . evaluation results indicate that our model achieves comparable performance on knowledge selection and response selection with several existing models trained on crowd-sourced benchmarks. our contributions are summarized as follows: ## related work early studies of retrieval-based dialogue focus on single-turn response selection where the input of a matching model is a message-response pair @xcite @xcite . recently, researchers pay more attention to multiturn context-response matching and usually adopt the representation-matching-aggregation paradigm to build the model. representative methods include the dual-lstm model @xcite , the sequential matching network (smn) @xcite , the deep attention matching network (dam) @xcite , interaction-overinteraction network (ioi) @xcite and multi-hop selector network (msn) @xcite . more recently, pre-trained language models @xcite have shown significant benefits for various nlp tasks, and some researchers have tried to apply them on multi-turn response selection. vig and ramea (2019) exploit bert to represent each utteranceresponse pair and fuse these representations to calculate the matching score; whang et al. (2020) and xu et al. (2020) treat the context as a long sequence and conduct context-response matching with bert. besides, gu et al. (2020a) integrate speaker embeddings into bert to improve the utterance representation in multi-turn dialogue. to bridge the gap of the knowledge between the human and the machine, researchers have investigated into grounding dialogue agents with unstructured background knowledge @xcite @xcite . for example, @xcite build a persona-based conversation data set that employs the interlocutor's profile as the background knowledge; @xcite publish a data where conversations are grounded in articles about popular movies; @xcite release another documentgrounded data with wiki articles covering a wide range of topics. meanwhile, several retrievalbased knowledge-grounded dialogue models are proposed, such as document-grounded matching network (dgmn) @xcite and dually interactive matching network (dim) @xcite which let the dialogue context and all knowledge entries interact with the response candidate respectively via the cross-attention mechanism. @xcite further propose to pre-filter the context and the knowledge and then use the filtered context and knowledge to perform the matching with the response. besides, with the help of gold knowledge index annotated by human wizards, @xcite consider joint learning the knowledge selection and response matching in a multi-task manner or training a two-stage model. ## model in this section, we first formalize the knowledgegrounded response matching problem and then introduce our method from preliminary to response matching with plms to details of three pre-training tasks. ## conclusion in this paper, we study response matching in knowledge-grounded conversations under a zeroresource setting. in particular, we propose decomposing the training of the knowledge-grounded response selection into three tasks and joint train all tasks in a unified pre-trained language model. our model can be learned to select relevant knowledge and distinguish proper response, with the help of ad-hoc retrieval corpora and amount of multiturn dialogues. experimental results on two benchmarks indicate that our model achieves a comparable performance with several existing methods trained on crowd-sourced data. in the future, we would like to explore the ability of our proposed method in retrieval-augmented dialogues.