How About Kind of Generating Hedges using End-to-End Neural Models? (2023)

Abstract: Hedging is a strategy for softening the impact of a statement in conversation. In reducing the strength of an expression, it may help to avoid embarrassment (more technically, “face threat”) to one’s listener. For this reason, it is often found in contexts of instruction, such as tutoring. In this work, we develop a model of hedge generation based on i) fine-tuning state-of-the-art language models trained on human-human tutoring data, followed by ii) reranking to select the candidate that best matches the expected hedging strategy within a candidate pool using a hedge classifier. We apply this method to a natural peer-tutoring corpus containing a significant number of disfluencies, repetitions, and repairs. The results show that generation in this noisy environment is feasible with reranking. By conducting an error analysis for both approaches, we reveal the challenges faced by systems attempting to accomplish both social and task-oriented goals in conversation.

## introduction when people interact, they attend not just to the task at hand, but also to their relationship with their interlocutors @xcite . one key aspect of the relationship that people attend to, while engaging in contexts as diverse as sales @xcite , education @xcite and healthcare @xcite , is what is referred to as rapport, a sense of harmony and mutual understanding between participants in a conversation @xcite . indeed, higher levels of rapport are correlated with better performance in each of these domains. @xcite describes rapport as built upon a base of mutual attentiveness, face management, and coordination. this base is built primarily by conversational strategies, or ways of speaking (including nonverbal and paraverbal behaviors) that yeah, so it will be ... i think you could add four to both sides. oh no... manage rapport throughout a conversation. key conversational strategies include self-disclosure, reference to shared experience, praise, and hedging -giving instructions or conveying information in an indirect manner when it might otherwise sound rude or overly demanding. end-to-end large language models (llm), of the kind that are increasingly popular and powerful, do a good job at carrying out the propositional or information-carrying aspects of conversation, and a relatively good job of maintaining the coherence of a conversation, but they are not as good at changing how they say something as a function of a relationship with the human user, while humans are, for the most part, quite good at this. however, since saying things in a specific manner -for example, through a hedge -helps task performance, it is an important topic for dialogue systems. linguists define hedges as a way of diminishing face threat (meaning the "positive social value a person effectively claims for himself" @xcite by attenuating the extent or impact of an expression @xcite . figure 1 shows a typical example of hedging in a peer tutoring setting, where the tutor uses two hedges ("i think" and "could" rather than "should") to deliver a hint for the next step of solving an algebra equation. tutoring is one context in which hedges are found in abundance and where recognizing them might be important for intelligent tutoring systems, as attested by the number of computational ap-proaches that attempt to do so (see section 2). interestingly, even unskilled tutors use them. in fact, research on peer tutoring has shown that when rapport between a peer tutor and tutee is low, but the tutor is confident in his/her skills, that tutor tends to use more hedges, and this results in more problems attempted by the student and more problems successfully solved @xcite . in this paper, then, we work towards the development of a generation module for a virtual peer tutor that, like real peer tutors, is able to choose the manner of delivering information in such a way. specifically, we address two research questions: rq1: how good are end-to-end large language models used alone for generating hedges when finetuned on a peer-tutoring dialogue dataset? are the models able to implicitly learn when and how to generate hedges? the first question may be answered by comparing the performance of various fine-tuned models. if the end-to-end models cannot learn to hedge implicitly, we might attempt to drive the models to generate the utterances by providing the correct labels. we assume that the correct labels can be provided by another module of the system, so we compare the reranking method with the fine-tuning method, as the former is simple, powerful, and widely used for text generation. consequently, the second question is: rq2: can we improve these models by using a reranking approach? if so, what are the remaining errors and why do they occur? ## related work considerably more computational methods exist to determine what a dialogue system should say than how to say it. however, more recently, with the increased power of end-to-end models to find information and convey it accurately, we can now turn to ensuring that the end-to-end model simultaneously also meets social goals, to increase the impact and acceptability of what is conveyed. ## conclusion and future work in this paper, we have shown that the reranking method helps llms to generate hedges -an important social conversational strategy that can avoid face threats towards an interlocutor by attenuating an impact of an expression. we find that an implicit fine-tuning approach (i.e., without any supervision by a hedge label) is not sufficient for generating hedges, but a reranking method significantly improves performance in generating hedges, with a final f1 score of .85 for the bart model and .84 for the blenderbot model. we also performed an error analysis on the generated results and found that two types of errors occur in the reranking method: classification, and goal mismatch. the vast majority of errors fall into the category of goal mismatch, indicating an important conflict between contemporary language models' primary goal of ensuring coherence and the social goal of managing face, which is indispensable for human conversation. while we were able to generate hedges, we were not able to necessarily generate them where they were needed most. that is, conversational strategies are adaptive in the sense that they respond to conversational strategies uttered by the previous speaker @xcite . we conclude that, going forward, we will need a way of adding an underlying representation of the social state of the dialogue to improve dialogue generation. in this paper we addressed the question of how to generate hedges, but when to generate hedges remains an important and unexplored question. in future work, we may first explore the temporal relationships between the hedge and other conversational information (e.g., other conversational strategies, level of rapport) by sequential rule mining techniques, then apply rl-based methods to investigate in a more detailed manner the optimal way to predict where hedges should occur. in this context, we note that chatgpt can generate a hedge when requested explicitly to do so, but does not generate hedges of its own volition (so to speak), for example, when face-threatening acts such as instruction are engaged in. we began this paper by describing the need for hedges in instructional dialogues such as those engaged in by intelligent tutoring systems. the current dataset consists of authentic real-world tutoring sessions, but as carried out by untrained teenagers. we note that peer tutoring is a powerful method of teaching, used in classrooms around the world, and previous work shows that when untrained peer tutors use hedges, their tutees attempt more problems and solve more problems correctly @xcite . however, they are inexperienced and so in future work it will be important to investigate the interaction between trained tutors and tutee as well, for instance, by using the teacher-student chatroom corpus @xcite . we believe that the methods and results from the current work will facilitate the investigation of expert tutors in future research.