Knowledge Enhanced Fine-Tuning for Better Handling Unseen Entities in Dialogue Generation (2021)

Abstract: Although pre-training models have achieved great success in dialogue generation, their performance drops dramatically when the input contains an entity that does not appear in pre-training and fine-tuning datasets (unseen entity). To address this issue, existing methods leverage an external knowledge base to generate appropriate responses. In real-world practical, the entity may not be included by the knowledge base or suffer from the precision of knowledge retrieval. To deal with this problem, instead of introducing knowledge base as the input, we force the model to learn a better semantic representation by predicting the information in the knowledge base, only based on the input context. Specifically, with the help of a knowledge base, we introduce two auxiliary training objectives: 1) Interpret Masked Word, which conjectures the meaning of the masked entity given the context; 2) Hypernym Generation, which predicts the hypernym of the entity based on the context. Experiment results on two dialogue corpus verify the effectiveness of our methods under both knowledge available and unavailable settings.

## introduction owing to large amounts of conversation data and pre-training models @xcite , generation-based chatbots have achieved significant advances and even reach human parity on specific testsets @xcite @xcite . however, the robustness of the pre-trained model is still low with regard to unseen entities @xcite . in practice, users often talk with chatbots about latest news and the recently hot topics @xcite , which may not appear in the pre-training or fine-tuning corpus. for instance, "covid-19" is a new term, which * contribution during internship at msra. does not appear in the training data of blender foot_0 @xcite , leading to poor performance when a user mentions "covid-19". as shown in figure 1 (a), given an utterance "i've tested positive for covid-19", blender yields a bad response "that's great news" because it misunderstands the utterance by the word "positive", which poses a real challenge for building an accurate and robust generation-based chatbot. existing methods leverage external knowledge to tackle the problem @xcite , where chatbots retrieve relevant knowledge about the entities from a external knowledge base and use the retrieved knowledge to help generating appropriated responses. however, these methods heavily depend on the coverage of the knowledge base and the accuracy of knowledge retrieval, which may fail when the entity is not included by the knowledge base @xcite or the retrieved knowledge is inappropriate @xcite . as shown in figure 1(b), the knowledge retriever fails to retrieve "covid-19" from the knowledge base, yielding an incorrect response. according to our statistics, the knowledge retrieval failure is not rare in real practice. taking reddit as an example, we collect 407 dialogues over 40 topics on the trendings panel and find that 24.8% of the topic words are polysemous, indicating the probability of incorrect knowledge retrieval, and 47.9% of topic words are not included by the wikipedia. to date, there are few studies that have investigated how to build a dialogue generation model within which knowledge may be unavailable during inference. we solve this problem by proposing a knowledge enhanced fine-tuning method, trying to understand semantic information of entities based on the context. for example, given the sentence "i want to submit a paper to emnlp", a person may not know what "emnlp" is, but he/she can guess that it should be a conference or a journal, based on the context. similarly, we aim to enhance the semantic representation of unseen entities by guiding the model to learn the meaning of the words only based on the context information. to achieve this, we take blender @xcite as our backbone model, and propose two auxiliary training objectives (figure 1(c )) in finetuning, dubbed as knowledge enhanced blender (ke-blender). the first objective is interpret masked word, which predicts the word's definition based on the context, where the definition is obtained from a knowledge base. the second is hypernym generation, which predicts the corresponding hypernym of the word given by wordnet. these two introduced training objectives force the model to learn semantic information from the external knowledge base during training, guessing the meaning of the unseen entity with its context, so as to better understand the input utterance and generate relevant responses during inference. both training objectives do not require further human labeling, which makes it possible for extending to large-scale pre-training. results on the wizard of wikipedia benchmark show that the proposed model brings performance improvement. the proposed method achieves 14.9 and 18.4 ppl on wizard test unseen in the knowledge available setting and unavailable setting, respectively, which outperforms the blender baselines (16.3 and 19.9 ppl). to further verify the effectiveness of our method in real-world scenarios, we collect 407 dialogues on the reddit trendings panel, demonstrating the effectiveness of the proposed method in practice. we release our code and dataset at https://github.com/nealcly/ ke-blender . ## analysis ablation study an interesting question is to explore the contribution of the two auxiliary losses in training. the results are shown in case study knowledge mining although we add two additional tasks in training, it is unclear how well the model performs in these two tasks. therefore, we further evaluate whether explicit knowledge can be recovered from our model given the unseen entity. first, we find that the perplexity of the ground-truth wikipedia knowledge on test unseen is only 6.81. as shown in