In dialogue state tracking (DST), the exploitation of dialogue history is a crucial research direction, and the existing DST models can be divided into two categories: full-history models and partial-history models. Since the “select first, use later” mechanism explicitly filters the distracting information being passed to the downstream state prediction, the partial-history models have recently achieved a performance advantage over the full-history models. However, besides the redundant information, some critical dialogue context information was inevitably filtered out by the partial-history models simultaneously. To reconcile the contextual consideration with avoiding the introduction of redundant information, we propose DICE-DST, a model-agnostic module widely applicable to the partial-history DST models, which aims to strengthen the ability of context exploitation for the encoder of each DST model. Specifically, we first construct a teacher encoder and devise two contextual reasoning tasks to train it to acquire extensive dialogue contextual knowledge. Then we transfer the contextual knowledge from the teacher encoder to the student encoder via a novel turn-level attention-alignment distillation. Experimental results show that our approach extensively improves the performance of partial-history DST models and thereby achieves new state-of-the-art performance on multiple mainstream datasets while keeping high efficiency.

Relation Classification (RC) is a basic and essential task of Natural Language Processing. Existing RC methods can be classified into two categories: sequence-based methods and dependency-based methods. Sequence-based methods identify the target relation based on the overall semantics of the whole sentence, which will inevitably introduce noisy features. Dependency-based methods extract indicative word-level features from the Shortest Dependency Path (SDP) between given entities and attempt to establish a statistical association between the words and the target relations. This pattern relatively eliminates the influence of noisy features and achieves a robust performance on long sentences. Nevertheless, we observe that majority of relation classification processes involve complex semantic reasoning which is hard to be achieved based on the word-level statistical association. To solve this problem, we categorize all relations into atomic relations and composed-relations. The atomic relations are the basic relations that can be identified based on the word-level features, while the composed-relation requires to be deducted from multiple atomic relations. Correspondingly, we propose theAtomic RelationEncoding andReasoningModel (ATERM). In the atomic relation encoding stage, ATERM groups the word-level features and encodes multiple atomic relations in parallel. In the atomic relation reasoning stage, ATERM establishes the atomic relation chain where relation-level features are extracted to identify composed-relations. Experiments show that our method achieves state-of-the-art results on the three most popular relation classification datasets – TACRED, TACRED-Revisit, and SemEval 2010 task 8 with significant improvements.

In dialogue state tracking, dialogue history is a crucial material, and its utilization varies between different models.However, no matter how the dialogue history is used, each existing model uses its own consistent dialogue history during the entire state tracking process, regardless of which slot is updated.Apparently, it requires different dialogue history to update different slots in different turns.Therefore, using consistent dialogue contents may lead to insufficient or redundant information for different slots, which affects the overall performance.To address this problem, we devise DiCoS-DST to dynamically select the relevant dialogue contents corresponding to each slot for state updating.Specifically, it first retrieves turn-level utterances of dialogue history and evaluates their relevance to the slot from a combination of three perspectives: (1) its explicit connection to the slot name; (2) its relevance to the current turn dialogue; (3) Implicit Mention Oriented Reasoning.Then these perspectives are combined to yield a decision, and only the selected dialogue contents are fed into State Generator, which explicitly minimizes the distracting information passed to the downstream state prediction.Experimental results show that our approach achieves new state-of-the-art performance on MultiWOZ 2.1 and MultiWOZ 2.2, and achieves superior performance on multiple mainstream benchmark datasets (including Sim-M, Sim-R, and DSTC2). 1

Jinyu Guo, Kai Shuang, Jijie Li, Zihan Wang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.