Label-Efficient Training for Next Response Selection (2020)

Abstract: This paper studies label augmentation for training dialogue response selection. The existing model is trained by “observational” annotation, where one observed response is annotated as gold. In this paper, we propose “counterfactual augmentation” of pseudo-positive labels. We validate that the effectiveness of augmented labels are comparable to positives, such that ours outperform state-of-the-arts without augmentation.

## introduction this paper studies the problem of response selection of the most appropriate answer given the dialogue history @xcite . a key challenge in this task is annotations being limited to "observational", most frequently annotating only one of such valid answers. meanwhile, linguistically diverse datasets are critical to ensure the robustness of machine learning models, though augmenting diverse expert annotations are often too costly to sustain, both in terms of (1) annotation and (2) training cost. for the first challenge of keeping annotation cost sustainable, there have been two directions: ## background in this section, we first define the response selection task and describe widely used baselines, namely bi-encoder @xcite architectures. ## multi-refrence training our proposed approach has a base architecture of @xcite , which adopts noisy student training paradigm @xcite ; park et al., 2020). recall that the observed annotation is @xmath0, r i )} n @xmath1, r i consists of one gold annotation, denoted as r i,1 , and t -1 negatively sampled examples. our goal is to expand d, a n × t matrix, into counterfactual observations of n × n matrix, where each context may have up to p positive labels. 1. train teacher model s (t ) on labeled dataset d 2. expand d into noisy paraphrases d 3. filter d by context-response matching s (t ) 4. train student model s (s) on the mix of ŝ(t ) (d ) and d. ## experiments the goal of our experiments is answering the following research questions. ## conclusion this paper studies the problem of label augmentation for response selection. our empirical results validate its effectiveness in both ranking and classification tasks.