Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues (2020)

Abstract: Incorporating commonsense knowledge can alleviate the issue of generating generic responses in open-domain generative dialogue systems. However, selecting knowledge facts for the dialogue context is still a challenge. The widely used approach Entity Name Matching always retrieves irrelevant facts from the view of local entity words. This paper proposes a novel knowledge selection approach, Prototype-KR, and a knowledge-aware generative model, Prototype-KRG. Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query. We find knowledge facts used in prototype dialogues usually are highly relevant to the current query; thus, Prototype-KR ranks such knowledge facts based on the semantic similarity and then selects the most appropriate facts. Subsequently, Prototype-KRG can generate an informative response using the selected knowledge facts. Experiments demonstrate that our approach has achieved notable improvements on the most metrics, compared to generative baselines. Meanwhile, compared to IR(Retrieval)-based baselines, responses generated by our approach are more relevant to the context and have comparable informativeness.

## introduction unlike human beings, generative dialogue systems tend to generate generic responses, such as 'i don't know.' @xcite . one possible reason is the gap in utilizing background knowledge. human beings can naturally frame their dialogue understanding and responding with various learned background knowledge during the conversation. however, traditional dialogue systems can merely access the surface knowledge in the given query @xcite . to tackle this issue, a feasible scheme is incorporating external knowledge into the dialogue generation @xcite . this paper focuses on introducing the structured open-domain commonsense knowledge graph into the single-turn dialogue response generation. commonsense knowledge refers to the widely-used everyday knowledge, for example, 'lemon tastes sour'. in general, a knowledge graph can be regarded as a set of (e head , r, e tail ) fact triplets. for the knowledge-aware dialogue generation, the first step is knowledge selection, aiming at selecting appropriate knowledge facts for the current dialogue context. traditional works @xcite always adopt the entity name matching (enm), i.e., knowledge facts are retrieved based on the entity words that appear in the given query. for example, the fact triplet (apple, isatypeof, fruit) can be selected for the query 'what's your favourite fruit?'. although such a widely-used method works to some extent, it has several flaws. first, only 1hop knowledge can be retrieved. second, instead of using the utterance-level (global) features, it uses local words to retrieve; thus, irrelevant knowledge facts may be selected. third, vertex (entity) degrees in a graph are always unequal; hence, once an entity in the query corresponds to a hot vertex, the number of selected facts can be tremendous. for the time efficiency, in the practical dialogue generation, we always have an upper bound to restrict the number of involved facts. consequently, a fact may be randomly discarded, no matter it is a highly relevant fact or an irrelevant fact; because enm can't judge the relevance of a retrieved fact. as shown in query oh, my phone is already broken. prototype's query your old phone is broken? prototype's response yes, so i bought a new iphone. prototype's knowledge (phone, related to, iphone) generated response it's time to buy a new iphone. our experiments are conducted on a large-scale chinese conversation dataset @xcite and a widely used commonsense knowledge graph conceptnet. the experimental results demonstrate our approach outperforms both generative baselines and ir-based baselines. we also conduct a series of extensive experiments to analyze the prototype-kr. we find our prototype-kr can retrieve higher-quality knowledge facts compared to the traditional entity name matching. our contributions can be summarized as 1)we propose a new knowledge selection approach, prototype-kr, which uses prototype dialogues to effectively alleviate the flaws of the traditional approach entity name matching; 2)we propose a knowledge-aware dialogue model, prototype-krg, for improving the knowledge-aware dialogue generation; 3) extensive experiments empirically verify the effectiveness of our approaches. knowledge-aware methods: one crucial factor that causes generating boring responses is the insufficiency of background knowledge. traditional models can merely access the surface knowledge from the plain text of the query @xcite . researchers have shown the generated dialogue responses can be more diverse and informative, by introducing the external knowledge, such as the unstructured background documents @xcite , structured knowledge graphs @xcite and knowledge tables @xcite , or the hybrid of them @xcite . knowledge selection: for the knowledge-aware dialogue generation, selecting appropriate knowledge facts from the knowledge graph for a specific dialogue context is still a challenge. as mentioned, the traditional entity name matching has many flaws, and thus many efforts have been devoted to enhancing this knowledge selection process. @xcite adopts a neural knowledge reasoning network to select an appreciate fact. @xcite transfers question representation and knowledge matching abilities from kbqa systems. although such works have achieved promising results, they always are not wise choices in the practical scenario. first, such approaches adopt complicated external networks to select knowledge, which would significantly increase parameters and make the training/inference more time-consuming. next, the external networks require a large amount of additional labeled data, which may not be an easy thing in practice. our work differs from them in that: 1) our approach does not require any additional data. prototype dialogues can be retrieved from the training corpus. 2) our ir-based knowledge selection is fast and requires no pre-training. prototypes: recently, the research of prototype dialogues has received much attention in the context of dialogue generation owing to its highquality. @xcite encodes a retrieved prototype dialogue into vectors, and then regards them as additional features to help the dialogue generation. @xcite generates a response by editing a prototype response. @xcite proposes a two-step skeleton-based dialogue generation. a notable shortage of such methods is they often use only one prototype dialogue @xcite ; thus, if the given prototype dialogue is irrelevant to the context, the generation quality will sharply decrease. besides, such methods sometimes may degenerate to directly copy the prototype response, rather than selectively extract useful information. in contrast to these works: 1) prototype-kr can utilize multiple prototypes at the same time; 2) prototype-krg is a fully generative approach, the dialogue generation process does not rely on copying or editing prototype dialogues. ## conclusion and future work we propose a novel knowledge selection method, prototype-kr, and a knowledge-aware model, prototype-krg, for the open-domain knowledgeaware dialogue generation. prototype-kr retrieves knowledge facts from the human-written prototype dialogues, which is fast, accurate and requires no additional labeled data. extensive experiments on a large-scale chinese dataset show that our approach outperforms generative baselines on most metrics. compared to ir-based approaches, our approach can generate responses with higher relevance and comparable informativeness. in the future, we will continue to strengthen the use of prototype dialogues without making the dialogue generation process complicated. meanwhile, we are going to research the possibility to use different knowledge in a dialogue system.