Modeling Transitions of Focal Entities for Conversational Knowledge Base Question Answering (2021)

Abstract: Conversational KBQA is about answering a sequence of questions related to a KB. Follow-up questions in conversational KBQA often have missing information referring to entities from the conversation history. In this paper, we propose to model these implied entities, which we refer to as the focal entities of the conversation. We propose a novel graph-based model to capture the transitions of focal entities and apply a graph neural network to derive a probability distribution of focal entities for each question, which is then combined with a standard KBQA module to perform answer ranking. Our experiments on two datasets demonstrate the effectiveness of our proposed method.

## introduction recently, conversational knowledge base question answering (kbqa) has started to attract people's attention @xcite @xcite . motivated by real-world conversational applications, particularly personal assistants such as apple siri and amazon alexa, the task aims to answer questions over kbs in a conversational manner. figure 1 shows an example of conversational kbqa. as we can see, the conversation can be roughly divided into two parts: q1, q2 and q3 revolve around the book "the great gatsby," while q4 and q5 revolve around its author, "f. scott fitzgerald". although these entities are not explicitly mentioned in the questions, they are implied by the conversation history, and they are critical for answering the questions. for example, q3, when taken out of context, cannot be answered because q3 itself does not state the title of the book being discussed. but since q3 is a follow-up question of q1, humans can easily infer that the book of interest here is "the great gatsby" and can hence answer the question correctly. we therefore can regard the entity "the great gatsby" as the focus of the conversation at this point. when we move on to q4, again, if the question is taken out of context, we cannot answer it. but by following the conversation flow, humans can guess that at this point the focus of the conversation has shifted to be "f. scott fitzgerald" (the answer to q3), and based on this understanding, humans would have no problem answering q4. we refer to "the great gatsby" and "f. scott fitzgerald" as the focal entities of the conversation. based on the observation above, we hypothesize that it is important to explicitly model how a conversation transits from one focal entity to another in order to effectively address the conversational kbqa task. there are at least two scenarios where knowing the current focal entity helps answer the current question. (1) the current focal entity is the unspecified topic entity foot_0 of the current question. e.g., "the great gatsby" is the unspecified topic entity for q3, which effectively should be "what is the name of the author of the great gatsby?" although existing work on conversational kbqa has tried to address the challenges of missing information in follow-up questions by modeling conversation history, most of it simply includes everything in the conversation history without considering focal entities. for example, @xcite leveraged a hierarchical encoder to encode all the questions and responses in the conversation history, but there was no explicit modeling of anything similar to focal entities. @xcite concatenated previous questions with the current question to fill in the missing information, but again there was no special treatment of entities. a more recent work @xcite believed that the answers to sequential questions should be closely connected to each other in the kb. thus, they proposed an algorithm to keep a context graph in memory, expanding it as the conversation evolves to increase the connections between the questions. however, their method is inefficient in capturing the most significant information related to focal entities in a conversation history. in this paper, we explicitly model the focal entities and their transitions in a conversation in order to improve conversational kbqa. based on several observations we have with focal entities, such as their tendencies to be topic entities or answer entities in the conversation history and their stickiness in a conversation, we propose to construct an entity transition graph to elaborately model entities involved in the conversation as well as their interactions, and apply a graph-based neural network to derive a focal score for each entity in the graph, which represents the probability of this entity being the focal entity at the current stage of the conversation. the key intuition behind the graph neural network is to propagate an entity's focal score in the i-th turn of the conversation to its neighboring entities in the (i + 1)-th turn of the conversation. this derived focal entity distribution is then incorporated into a standard single-turn kbqa system to handle the current question in the conversation. we evaluate our proposed method on two conversational kbqa datasets, convquestions @xcite and convcsqa (which is a subset we derived from csqa @xcite ). experiment results show that compared with either a single-turn kbqa system or a system that simply encodes the entire conversation history without handling focal entities in a special way, our method can clearly perform better on both datasets. our method also outperforms several existing systems that represent the state of the art on these benchmark datasets. we also conduct error analysis that sheds light on where further improvement is desired. we summarize our contributions of this paper as follows: (1) we propose to explicitly model the focal entities of a conversation in order to improve conversational kbqa. (2) we propose a graphbased neural network model to capture the transitions of focal entities and derive a focal entity distribution that can be plugged into a standard single-turn kbqa system. (3) we empirically demonstrate the effectiveness of our method on two datasets. our method can outperform the state of the art by 9.5 percentage points on convquestions and 14.3 percentage points on convcsqa foot_1 . ## experiments in this section, we first introduce two benchmark datasets and our experiment settings in section 4.1 and section 4.2. next, we discuss the main results and analysis in section 4.3 and section 4.4. we further show the comparison with sota systems in section 4.5 and some error analysis in section 4.6. ## related work single-turn kbqa task has been studied for decades. traditional methods tried to retrieve the correct answers from the kb via either embeddingbased methods @xcite @xcite @xcite or semantic parsing-based methods @xcite @xcite @xcite . conversational kbqa is a relatively new direction that builds on top of single-turn kbqa. 8 since the original d2a and masp codes leverage the ground truth topic entities and relations to pre-train the entity linker and relation predictor but we do not, we skip the pretraining procedure in our re-implementation. conversational kbqa is related to dialogue systems and conversational qa in general, which require techniques to sequentially generate responses based on the interactions with users @xcite @xcite . a conversation history can be encoded via different techniques such as a hierarchical neural network @xcite or modeling the flow of the conversation along with a passage @xcite @xcite . our work also intends to capture the flow of the conversation but we specifically model the transitions of focal entities. regarding conversational kbqa, @xcite proposed a model consisting of a hierarchical encoder, a key-value memory network and a decoder. @xcite and @xcite employed a seq2seq model to encode the conversation history then output a sequence of actions to form an executable command. some follow-up work @xcite focused on the meta-learning setting or the effective search strategy under weak supervision, which is beyond the focus of this paper. @xcite detected frontier nodes by expanding a subgraph, which are potential answer entities to the current question. their motivation is relevant to ours but we target at modeling the focal entities in the conversation. ## conclusion in this paper, we present a method to model the transitions of focal entities in a conversation in order to improve conversational kbqa. our method can outperform two baselines and achieve state-ofthe-art performance on two benchmark datasets.