Grounding in social media: An approach to building a chit-chat dialogue model (2022)

Abstract: Building open-domain dialogue systems capable of rich human-like conversational ability is one of the fundamental challenges in language generation. However, even with recent advancements in the field, existing open-domain generative models fail to capture and utilize external knowledge, leading to repetitive or generic responses to unseen utterances. Current work on knowledge-grounded dialogue generation primarily focuses on persona incorporation or searching a fact-based structured knowledge source such as Wikipedia. Our method takes a broader and simpler approach, which aims to improve the raw conversation ability of the system by mimicking the human response behavior through casual interactions found on social media. Utilizing a joint retriever-generator setup, the model queries a large set of filtered comment data from Reddit to act as additional context for the seq2seq generator. Automatic and human evaluations on open-domain dialogue datasets demonstrate the effectiveness of our approach.

## introduction humans have long wanted to talk with the machine and have them comprehend and generate natural language. the task of chit-chat dialogue response generation can be described as one of the major goals in natural language processing. as such, there has been considerable interest in the sub-field of open-domain dialogue models. nevertheless, the existing dialogue response generation models still suffer from some very fundamental problems: lack of interesting ("ok", "i see", etc.) or uninformative responses ("i don't know") @xcite @xcite . the primary cause for this is that, unlike humans, the models do not have access to knowledge, experience about out-of-domain topics or human conversational habits and hence can only produce limited unengaging generic responses. recent work has proposed considering additional context information such as multi-turn conversational history @xcite , persona @xcite or a fact-based knowledge base @xcite . among these, our work approaches this problem from a more general standpoint of improving the raw conversational ability of generative models. we attempt this by taking inspiration from how humans learn to converse, i.e., through mimicking social interactions. applying this in the context of dialogue models, we use a human-readable external knowledge base consisting solely of unstructured social media interactions (hereinafter referred to as smikb), which tends to include a more diverse language structure and hence improve generated responses. for our approach, we jointly train a generatorretriever model where the retriever searches through pre-indexed smikb and feeds the related information together with the input utterance to the generative seq2seq model, allowing for additional context at the time of generation. in particular, we utilize the dense passage retriever proposed by @xcite on top of bart @xcite as our generational model trained on a mix of open-domain dialogue datasets, together with a collection of reddit submissions and comments as our main source of social interactions. experiments showed that our approach outperformed the existing vanilla seq2seq baseline (bart) across all of the automatic and human evaluation metrics. by making use of interactions grounded in social media, the generated responses were not only more engaging but were also shown to be much more relevant and natural, thus establishing the effectiveness of our approach. ## proposed approach in this section, we discuss our approach to introducing social media interactions as an external knowledge base (smikb) to ground in for more natural and human-like response generation. we begin with formulating the task of dialogue generation and then proceed to explain our joint retrievergenerator model as the proposed setup for utilizing the aforementioned unstructured data source. note that in this work, we primarily focus on response generation for single-turn dialogues or dialogues. we decided that other settings such as a multi-turn case were best addressed in future work. ## experiments we evaluate our model together with various external knowledge datasets on a mixture of opendomain dialogue datasets. the results are then compared with two bart-based baselines. ## conclusion we aimed to improve the raw conversational ability of dialogue systems by grounding the responses in much more human-like social media interactions. our approach involved a neural retriever-seq2seq generator model fine-tuned jointly, where relevant knowledge was retrieved at the time of generation to assist a more engaging and natural dialogue response. our experiments showed significant improvements with both automatic and human evaluation metrics ranking the smikb-grounded replies to be much more diverse, engaging, and relevant.