Logic Unveils Truth, While Disguise Obscures It: Transition Logic Augmented Response Selection for Multi-Turn Dialogue (2023)

Abstract: Multi-turn response selection aims to retrieve a response for a dialogue context from a candidate pool and negative sampling is the key to its retrieval performance. However, previous methods of negative samples tend to yield false negatives due to the one-to-many property in open-domain dialogue, which is detrimental to the optimization process. To deal with the problem, we propose a sequential variational ladder auto-encoder to capture the diverse one-to-many transition pattern of multiple characteristics in open-domain dialogue. The learned transition logic thus assists in identifying potential positives in disguise. Meanwhile, we propose a TRIGGER framework to adjust negative sampling in the training process such that the scope of false negatives dynamically updates according to the model capacity. Extensive experiments on two benchmarks verify the effectiveness of our approach.

## introduction recently, retrieval-based dialogue draws rising interest from the nlp community @xcite @xcite , since it is a promising way towards intelligent human-machine dialogue. moreover, the technologies in building it show great potential in various applications such as tasked-oriented dialogue assistant @xcite , conversational recommendation @xcite or the recently released interactive large language model @xcite . in this study, we focus on the core of a retrieval dialogue system, i.e., the multi-turn response selection task, which aims to retrieve the best response (golden response) from a pre-defined candidate pool given a dialogue context. for improving the discriminating power of a retrieval dialogue system, the key is the construction of the candidate pool, or the selection of the negative examples. since the trivial solution, i.e., randomly sampling utterances from the entire training set, results in too simple and informative negatives @xcite , a large body of previous works @xcite @xcite ) discuss how to excavate hard negatives that are lexically or semantically similar to the golden response and therefore hard to differentiate. however, due to the inherent one-to-many property of open-domain dialogue, sometimes hard negatives are in fact positive in disguise, or false negatives @xcite and are detrimental to the convergence of retrieval model @xcite . to verify this point, we perform a pilot study (section 2) to find that previous negative sampling methods do yield a portion of false negatives and the ratio is higher than random sampling. it is easy to understand since previous methods tend to overlook and thus have little control over the false negative issue, except that @xcite try to filter out the false negatives in a heuristic manner. nevertheless, heuristically filtering out negatives that are similar to the golden response is far from enough, and mitigating the false negative issue is a non-trivial problem: a major challenge is that a randomly sampled utterance could also be more or less appropriate, although it may differ from the golden response in many aspects. owing to the one-to-many nature @xcite , there usually exists more than one possible dialogue flow, with each flow reflecting different transitions in dialogue topic, user emotion and many other characteristics. to recognize and mitigate false negatives, it is required that we capture the diverse potential transition logic of multiple characteristics in open-domain dialogue, which is difficult to acquire @xcite . another challenge lies in the balance between excavating hard negatives and removing false negatives @xcite . specifically, we may largely avoid false negatives by always selecting naive negative examples that are obviously inappropriate, which is however uninformative and useless. in other words, the dividing line between the false negatives and the hard negatives is bounded to change dynamically in the training process according to the retrieval model capacity. though there exist some works employing curriculum learning @xcite , their adjustment of negative sampling is performed in an empirical way independent of the model capacity. in this research, to cope with the first challenge, we propose to decompose the characteristics in multi-turn conversation into multiple dimensions and represent each with a latent label respectively. to achieve this, we design a sequential variational ladder auto-encoder (svlae) to model the transition logic of multiple characteristics and disentangle each other. for the second challenge, we update the negative sampling dynamically in the training process in pace with the optimization of the retrieval model. specifically, we propose a trigger (transition logic augmented retrieval) framework, which consists of t-step and r-step and optimizes the negative sampling process and retrieval model in two steps iteratively. to summarize, our contributions are three-fold: ## pilot study on false negatives in this pilot study, we conduct a human evaluation to investigate false negatives hidden in the candidate pool, where the negative samples in the candidate pool are constructed by random sampling or model false negative ratio random 3.29 semi @xcite 7.16 cir @xcite 7.03 grey @xcite 7.42 hcl @xcite 6.95 mask-and-fill @xcite 5.63 @xcite and for every context we sample 5 negatives from each sampling method. the experiment results are shown in ## related work the task of multi-turn response selection aims at selecting a response to match the human input from a large candidate pool @xcite @xcite @xcite @xcite . close related to conversational recommendation @xcite and interactive large language model (ope-nai, 2022), it has extensive application in the commercial area. with the recent huge success of plms @xcite , posttraining plms with diverse self-supervised tasks become a popular trend and achieve impressive performance @xcite @xcite @xcite . apart from designing new architecture or new self-supervision task, another branch of work put emphasis on negative sampling. namely, since the quality of negative candidates has a great influence on the retrieval model, a large body of work curate hard negative candidate by searching within the corpus @xcite , synthesizing from language model @xcite or a combination of both @xcite . however, most previous methods pay little attention to preventing false negatives or updating the negative sampling to adapt with the optimization of the retrieval model, albeit the semantic limitation in @xcite and the curriculum learning in @xcite and @xcite . the comparison of our methods against previous ones is shown in to enable a retrieval model to differentiable multiple candidates and pick out the r + , the core is the meticulous construction of the candidate pool r. overview. the proposed approach has two stages: (1) transition logic estimation and (2) dynamic negative updating. in the first stage, we train a transition model to capture the transition logic of multi-level characteristics in a conversation. the transition model is used for detecting characteristics in multiple facets to decide whether a candidate utterance is a potential false negative. however, with the growing model capacity, the criteria for potential false negatives should change accordingly. so in the second stage, we introduce a policy network to determine the negative sampling criteria regarding multi-facet characteristics according to the feedback from the retrieval model. in this way, the negative sampling process paces with the evolution of the retrieval model. at the test stage, the transition model and the policy network are discarded so it causes no extra latency.