Knowledge-augmented Self-training of A Question Rewriter for Conversational Knowledge Base Question Answering (2022)

Abstract: The recent rise of conversational applications such as online customer service systems and intelligent personal assistants has promoted the development of conversational knowledge base question answering (ConvKBQA). Different from the traditional single-turn KBQA, ConvKBQA usually explores multi-turn questions around a topic, where ellipsis and coreference pose great challenges to the single-turn KBQA systems which require self-contained questions. In this paper, we propose a rewrite-and-reason framework to first produce a full-fledged rewritten question based on the conversation history and then reason the answer by existing single-turn KBQA models. To overcome the absence of the rewritten supervision signals, we introduce a knowledge-augmented self-training mechanism to transfer the question rewriter from another dataset to adapt to the current knowledge base. Our question rewriter is decoupled from the subsequent QA process, which makes it easy to be united with either retrieval-based or semantic parsing-based KBQA models. Experiment results demonstrate the effectiveness of our method and a new state-of-the-art result is achieved. The code and dataset are available online now.

## introduction knowledge base question answering (kbqa) has been a recent surge of research interest, due to the appearance of large-scale knowledge bases (kbs), such as wikidata @xcite , freebase @xcite , as well as some domain kbs such as the amazon product kb @xcite and the academic kb @xcite . kbqa provides users an easier way to seek for factual knowledge in the natural language in spite of kbs' underlying structures. however, q1: who wrote the great gatsby? a1: f. scott fitzgerald q2: what is the publish date? a2: 10 april 1925 q3: who was fitzgerald married to? a3: zelda fitzgerald q4: where were the fitzgeralds married? a4: st. patrick's cathedral q5: what city is it in? a5: new york city who wrote the great gatsby? what is the publish date of the great gatsby? who was f. scott fitzgerald married to? where were f. scott fitzgerald and zelda fitzgerald married? what city is st. patrick's cathedral in? instead of the single-turn kbqa requirement, people tend to start from a topic and explore it with follow-up questions in a conversational manner. especially, driven by the rise of practical conversational applications such as online customer service system and intelligent personal assistant, conversational kbqa (convkbqa) has been attracting more attention. convkbqa poses great challenges for existing qa systems, which target to process a single selfcontained question once a time. because, in a conversational setting, the follow-up questions are usually incomplete with missing entities, which is referenced as ellipsis and coreference phenomenon. figure 1 shows an example of convkbqa, where the initial question is usually full-fledged mentioning clear topic entities, from which the conversation often drifts away. in this example, it revolves around three subjects alternately: the book, its author and the cathedral. question q2 that omits the title of the book is an instance of entity ellipsis. q3-q5 have entity coreference phenomenon because "fitzgerald" in q3 and "fitzgeralds" in q4 refer to "f. scott fitzgerald" and the couple respectively. to facilitate such conversation, we can replace or supplement with the entities they refer to or leave out to make them self-contained. that is to say, the follow-up questions can be rewritten as shown on the right in figure 1 . existing works leverage the conversation history as well as the underlying kb to overcome the above ellipsis and coreference problem. similar to the single-turn kbqa @xcite , the convkbqa methods can be categorized into the retrieval-based @xcite and semantic parsing (sp)-based ones @xcite @xcite . the former ones first identify the topic entities for each turn's question, and then expand a subgraph for answer reasoning. they usually maintain a gradually growing topic entity candidate set during the conversation, but take the risk of redundant and noisy candidates as well as error propagation caused by the identified candidates of previous turns. the latter ones parse each turn's question into an executable logic form, but suffer from the lack of the annotated logic forms and the incompleteness of the questions. they usually augment the input with conversation history and extra kb information, but the long textural input and its distribution difference with the logical output increase the difficulty of parsing. this paper proposes rewrite-and-reason, a different pipeline framework by first rewriting the incomplete question into a self-contained one and then adopting any single-turn kbqa model. compared with the existing retrieval-based methods, the rewriter in the first step of the framework does not reply on entity linking, and the rewritten questions of previous turns do not affect the next turn, which reduces error propagation. besides, the rewriter can maintain the original input format (i.e., the natural language question) to further enable sp-based single-turn kbqa models in the second step. compared with the sp-based convkbqa models that directly parse an incomplete question, such decoupled rewrite-and-reason (rewrite-and-parse if the sp-based model is used for reasoning) can reduce the parsing difficulty. since the annotations of the rewritten questions are unavailable in the target convkbqa dataset, we take advantage of existing annotations in other conversational qa (convqa) datasets as the in-herent regular patterns of ellipsis and coreference are similar in conversation. however, the conversation styles are quite different that convqa is more chatty and verbose while convkbqa is more knowledge-centric and concise. even worse, when facing the entities and relations not involved before, the rewriter might be at a loss. so, we further fine-tune it by knowledge-augmented self-training, where the augmented knowledge exposes the current underlying kb to the rewriter while the selftraining process tries to adapt the rewriter to the current conversation style. the idea can also be applied to using other annotated datasets for training the subsequent sp-based kbqa models. we conduct extensive experiments on convquestions @xcite , a convkbqa dataset grounded in wikidata. the results reveal three major advantages: (1) the question rewriter, equipped with a retrieval-based reasoner nsm @xcite or a sp-based reasoner kopl @xcite both achieves significant gains (+11.6% and +3.1% hits@1 respectively) and creates new state-of-the-art results. (2) compared with existing convkbqa systems, the rewritten questions can help identify the topic entities which results in a higher answer coverage rate (+12.9%). (3) ablation studies demonstrate the knowledge-augmented self-training can indeed make the pre-trained rewriter/kopl adapt to the concerned convkbqa task. contributions . (1) we propose a question rewriter decoupled from the subsequent kbqa model to enable a plug-and-play framework for convkbqa, which can adopt both the retrieval-and the spbased single-turn kbqa models. (2) we devise a knowledge-augmented self-training strategy for adapting the pre-trained question rewriter to the concerned conversation style and the underlying kbs. (3) both nsm and kopl equipped with the rewriter achieve new sota performance on the well-adopted benchmark convquestions. ## related work single-turn kbqa falls into two mainstream methods, retrieval-based @xcite @xcite and sp-based @xcite @xcite . the former ones usually encode the entities and relations in kbs as well as the questions into a unified embedding space, based on which the answers are inferred. recently, these pseudo programs selfcontained questions pre-trained rewriter fine-tuned rewriter knowledgeaugmented self-training pre-train rewriter on the open domain convqa dataset infer high-quality rewritten questions on the convkbqa dataset find related (head, relation, tail) for conversation history k no w le dg e ps eu do lab els rewrite the questions in convkbqa dataset train retrieval-based reasoner retrieve the subgraph pre-train sp-based reasoner self-train sp-based reasoner reasoner question rewriter train the relation retriever on pseudo (question, relation) pairs relation retriever subgraph figure 2 : overview of the rewrite-and-reasoner framework. a question rewriter is pre-trained on the open domain convqa dataset and is fine-tuned on the concerned convkbqa dataset by knowledge-augmented self-training, where the knowledge is obtained by a relation retriever that is trained on pseudo (question, relation) pairs. given the self-contained questions output by the rewriter, we train a retrieval-based reasoner on the retrieved subgraph, and also train a sp-based reasoner by the similar pre-training and fine-tuning paradigm. conversational kbqa follows traditional singleturn kbqa systems and can also be categorized into retrieval-and sp-based methods. different from the single-turn kbqa, the crux here is to deal with the ellipsis and coreference problem. the former ones usually identify the topic entities of each turn's question and then retrieve the subgraph for answer reasoning. the major differences lie in how to identify the topic entities. for example, @xcite builds an entity transition graph and applies a graph neural network to derive the topic entity distribution, while @xcite defines four heuristic measures to estimate such distribution. the sp-based methods @xcite @xcite also parse from each turn's question to the logic form similar to the single-turn kbqa, but the difficulty is the incompleteness of the questions to be parsed. so they augment the model input with conversation history as well as extra kb information for parsing out the more correct logic form. but meanwhile, it inevitably introduces additional noises which might hamper the parsing performance. conversational qa performs conversational qa over unstructured text data instead of the structured kb. thus different from convkbqa, con-vqa usually performs question rewriting, document retrieval, and reading comprehension on text data @xcite . besides, the conversations in convqa are more chatty than those in convkbqa. such difference also hinders us from directly using the annotations in convqa to train convkbqa models. ## the rewrite-and-reason framework in this section, we first introduce the proposed rewrite-and reason framework, and then elaborate on each part of our model, i.e., the pre-training and fine-tuning of the question rewriter, as well as the downstream reasoners used in our work. ## conclusion this paper proposes a rewrite-and-reason framework to address the ellipsis and coreference for convkbqa. to overcome the lack of annotations, we introduce a knowledge-augmented self-training mechanism for training the question rewriter. thanks to the decoupled design, we can unite the rewriter with both the retrieval-and sp-based single-turn kbqa models. the experiment results show that our method can outperform existing con-vkbqa systems and achieve new state-of-the-art. the model is trained for 5 epochs with the batch size as 10, where for each of the 10 questions, we retrieve the positive relations following figure 4 and complete 64 relations in total by negative sampling. we save the checkpoint obtaining the best h1 on the validation set. a training epoch takes about 15-20 minutes and it takes about 1-2 hours for the model to converge. a.4 question rewriter pre-training. we use t5-base 10 as the backbone of the question rewriter and finetune it with the transformer trainer 11 on canard @xcite . we set the learning rate as 1e-4, the gradient accumulation steps as 4, the batch size as 32, and the epochs as 5. a training epoch takes about 8-12 minutes and it takes about 1 hour for the model to converge.