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Register a new userAutomatically Exposing Problems with Neural Dialog Models
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Added by
Dian Yu
Publication date
2021
fields
Informatics Engineering
and research's language is
English
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Neural dialog models are known to suffer from problems such as generating unsafe and inconsistent responses. Even though these problems are crucial and prevalent, they are mostly manually identified by model designers through interactions. Recently, some research instructs crowdworkers to goad the bots into triggering such problems. However, humans leverage superficial clues such as hate speech, while leaving systematic problems undercover. In this paper, we propose two methods including reinforcement learning to automatically trigger a dialog model into generating problematic responses. We show the effect of our methods in exposing safety and contradiction issues with state-of-the-art dialog models.
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Despite the remarkable success deep models have achieved in Textual Matching (TM), their robustness issue is still a topic of concern. In this work, we propose a new perspective to study this issue -- via the length divergence bias of TM models. We conclude that this bias stems from two parts: the label bias of existing TM datasets and the sensitivity of TM models to superficial information. We critically examine widely used TM datasets, and find that all of them follow specific length divergence distributions by labels, providing direct cues for predictions. As for the TM models, we conduct adversarial evaluation and show that all models performances drop on the out-of-distribution adversarial test sets we construct, which demonstrates that they are all misled by biased training sets. This is also confirmed by the textit{SentLen} probing task that all models capture rich length information during training to facilitate their performances. Finally, to alleviate the length divergence bias in TM models, we propose a practical adversarial training method using bias-free training data. Our experiments indicate that we successfully improve the robustness and generalization ability of models at the same time.
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This paper addresses the question: Why do neural dialog systems generate short and meaningless replies? We conjecture that, in a dialog system, an utterance may have multiple equally plausible replies, causing the deficiency of neural networks in the dialog application. We propose a systematic way to mimic the dialog scenario in a machine translation system, and manage to reproduce the phenomenon of generating short and less meaningful sentences in the translation setting, showing evidence of our conjecture.
Dialog state tracking (DST) suffers from severe data sparsity. While many natural language processing (NLP) tasks benefit from transfer learning and multi-task learning, in dialog these methods are limited by the amount of available data and by the specificity of dialog applications. In this work, we successfully utilize non-dialog data from unrelated NLP tasks to train dialog state trackers. This opens the door to the abundance of unrelated NLP corpora to mitigate the data sparsity issue inherent to DST.
The design of better automated dialogue evaluation metrics offers the potential of accelerate evaluation research on conversational AI. However, existing trainable dialogue evaluation models are generally restricted to classifiers trained in a purely supervised manner, which suffer a significant risk from adversarial attacking (e.g., a nonsensical response that enjoys a high classification score). To alleviate this risk, we propose an adversarial training approach to learn a robust model, ATT (Adversarial Turing Test), that discriminates machine-generated responses from human-written replies. In contrast to previous perturbation-based methods, our discriminator is trained by iteratively generating unrestricted and diverse adversarial examples using reinforcement learning. The key benefit of this unrestricted adversarial training approach is allowing the discriminator to improve robustness in an iterative attack-defense game. Our discriminator shows high accuracy on strong attackers including DialoGPT and GPT-3.
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