D - REX : Dialogue Relation Extraction with Explanations (2022)

Abstract: Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled explanations. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that optimizes for explanation quality and relation extraction simultaneously. We frame relation extraction as a re-ranking task and include relation- and entity-specific explanations as an intermediate step of the inference process. We find that human annotators are 4.2 times more likely to prefer D-REX’s explanations over a joint relation extraction and explanation model. Finally, our evaluations show that D-REX is simple yet effective and improves relation extraction performance of strong baseline models by 1.2-4.7%.

## introduction traditional relation extraction (re) approaches discover relations that exist between entities within a single sentence. recently, several approaches have been proposed which focus on cross-sentence re, the task of extracting relations between entities that appear in separate sentences @xcite @xcite as well as cross-sentence re in dialogues @xcite @xcite @xcite . a crucial step towards performing crosssentence re in multi-entity and multi-relation dialogues is to understand the context surrounding relations and entities (e.g., who said what, and to whom). figure 1 shows an example from the di-alogre dataset where a simple bert-based model (initial predicted relation in figure 1 ) gets confused by multiple entities and relations existing in the same dialogue @xcite . the model predicts the "girl/boyfriend" relation between speaker 2 and chandler, however, it is clear from the context that the "girl/boyfriend" relation is referring to a different pair of entities: speaker 1 and chandler. one approach to encourage a model to learn the context surrounding a relation is by requiring the model to generate an explanation along with the relation @xcite . in addition to the dialogre dataset, @xcite introduces manually annotated trigger words which they show play a critical role in dialogue-based re. they define trigger words as "the smallest span of contiguous text which clearly indicates the existence of the given relation". in the context of re, these trigger words can be used as potential explanations. our work aims to extract explanations that clearly indicate a relation while also benefiting an re model by providing cross-sentence reasoning. our proposed approach, d-rex, makes use of multiple learning signals to train an explanation extraction model. first, d-rex utilizes trigger words as a partial supervision signal. additionally, we pro-pose multiple reward functions used with a policy gradient, allowing the model to explore the explanation space and find explanations that benefit the re-ranking model. including these reward functions allows d-rex to learn meaningful explanations on data with less than 40% supervised triggers. in order to predict relation-and entity-specific explanations in d-rex, we pose re as a relation re-ranking task with explanation extraction as an intermediate step and show that this is not possible for a model trained to perform both tasks jointly. our contributions are summarized as follows: ## problem formulation we follow the problem formulation of yu et al.: let @xmath0: u 1 , s 2 : u 2 , . . . , s n : u n ) be a dialogue where s i and u i denote the speaker id and the utterance from the i th turn, respectively. let e, r be the set of all entities in the dialogue and the set of all possible relations between entities, respectively. each dialogue is associated with m relational @xmath1, r, @xmath2, @xmath3. each relational triple may or may not be associated with a trigger t. it is important to note that there is no restriction on the number of relations held between an entity pair; however, there is at most one trigger associated with a relational triple. in this work, we consider an explanation to be of high quality if it strongly indicates that a relation holds, and for this purpose we consider triggers to be short explanations, though not always optimal in quality. ## baseline models we first introduce approaches for re and ee based on state-of-the-art language models. we then propose a multitask approach that performs both tasks jointly. our approaches use bert base @xcite and roberta base @xcite pretrained models foot_1 , and follow their respective finetuning protocols. for all models, we maintain a single input format, which follows from yu et al.. formally, for a dialogue d, subject s, object o, relation r, and explanation ex, the input sequence to all models is [cls]{r/ex[sep]}s[sep]o[sep]d, where {r/ex[sep]} denotes that the relation or explanation may be included depending on the task setting. for roberta models, we use @xmath4, respectively. ## d-rex in this section, we introduce the d-rex system. we begin by introducing the models which make up the system. next, we present the training and inference algorithms. finally, we discuss the optimization objectives for each model in the system. ## experimental evaluation in this section, we present an evaluation of d-rex in comparison with baselines methods on the relation extraction and explanation extraction tasks. ## limitations and future work firstly, this study focuses on learning explanations as well as relations in dialogue and dialogre is the only currently available dataset with annotations for both tasks. a limitation of this study is the small scale at which we were able to test the methods. a future direction would be to learn explanations on a different re dataset and use the pre-trained model in d-rex, however it would be non-trivial for a model to transfer explanations learned on a wildly different domain. additionally, it is theoretically possible to train d-rex with no labeled triggers at all, however, we were unsuccessful and in section 5.6 we discuss these and additional negative results. this study focuses on relations and entities found in multi-party conversations, and while there are similarities between the dialogue domain, medical literature, and wikipedia (e.g., multi-entity, multi-relation), it is not clear whether the methods from this paper can transfer to other such domains. we plan to investigate how well the proposed methods transfer to relations and entities in other domains such as news and web text @xcite and for other types of semantic relations as in @xcite or @xcite . we acknowledge that this study is englishfocused, and it is not clear that these methods can transfer to languages in other families such as afroasiatic or sino-tibetan. additionally, we think that it would be very interesting to see how these methods perform on languages with very different linguistic features; for example, languages with inflection such as finnish. we leave non-english and multilingual variations of these methods to future work. in this work, we do not focus on improving stateof-the-art trigger prediction. however, we recognize that trigger annotation is labor-intensive, and a possible use of d-rex would be to use predicted labels as a form of weak supervision for a system whose goal is to improve on trigger prediction. ## related work recently, there have been numerous information extraction tasks proposed which involve dialogues, including character identification @xcite , visual coreference resolution @xcite , emotion detection (zahiri and choi, 2018). new settings for relation extraction have also been proposed, such as web text @xcite and, in many ways similar to dialogue, document text @xcite . there have also been methods developed to include explanations in similar natural language understanding tasks @xcite @xcite . there have even been methods developed which, similarly to our reranking, make use of an explanation as additional information @xcite . the work by shahbazi et al. is aligned with our study. they also focus on relation extraction with explanations; however, their method is based on distant supervision from bags of sentences containing an entity-pair. due to the cross-sentence nature of relations in dialogue, their method is not applicable here, although we draw inspiration from their work. they explain their model by considering the salience of a sentence to their model's prediction, similarly to our leave-one-out reward. also relevant to our work is that by bronstein et al.. their work focuses on the task of semisupervised event trigger labeling, which is very similar to our semi-supervised prediction of relation explanations. in their work, they use only a small seed set of triggers and use a similarity-based classifier to label triggers for unseen event types. finally, there have been multiple recent works in dialogue re which perform quite well by using graph neural networks @xcite @xcite . however, they focus only on re and not on explaining the relations. ## conclusion in this work, we demonstrated that not only is it possible to extract relation explanations from multiparty dialogues, but these explanations can in turn be used to improve a relation extraction model. we formulated purpose-driven reward functions for training the explanation model and demonstrated their importance in learning high quality explanations. our proposed approach, d-rex, is powered by a very simple reformulation of the traditional relation extraction task into a re-ranking task.