Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery (2023)

Abstract: In this paper, we conduct the first study on spurious correlations for open-domain response generation models based on a corpus CGDialog curated by ourselves. The current models indeed suffer from spurious correlations and have a tendency to generate irrelevant and generic responses. Inspired by causal discovery algorithms, we propose a novel model-agnostic method for training and inference using a conditional independence classifier. The classifier is trained by a constrained self-training method, coined ConSTrain, to overcome data sparsity. The experimental results based on both human and automatic evaluation show that our method significantly outperforms the competitive baselines in terms of relevance, informativeness, and fluency.

## introduction open-domain response generation models have achieved impressive empirical success due to the recent advances in large-scale pre-trained transformers @xcite . however, although those models can generate fluent responses, it is still difficult for them to deeply understand conversation histories, and produce coherent and semantically diverse responses, especially when the conversation histories are long @xcite . we conjecture that one of the key reasons is spuriously correlated utterances in histories, which do not directly result in responses. although the vulnerability to spurious correlations is a well-known problem in deep learning models @xcite , to the best of our knowledge, there is no study on this topic from a causal perspective for response generation models. to investigate spurious correlations in dialogues, we are concerned with identifying non- * corresponding author. causal discovery algorithms provide a theoretically grounded way to learn causal relations between random variables from observational data @xcite . although they can be applied to identify which utterances in conversation histories are direct causes of responses in theory, the research on such methods for natural language processing problems is still in its infancy. in this work, we conduct the first study on spurious correlations for response generation models from a causal perspective. we empirically show that non-cause utterances, including spurious correlated ones, have significantly more influence on response generation models than the direct cause utterances human would rely on. inspired by causal discovery algorithms, we propose a model-agnostic training and inference method for mitigating spurious correlations in long conversations. the method aims to automatically identify key utterances in histories, which serve as direct causes for response generation. herein we convert the cause identification problem into a problem of conditional independence (ci) tests. the ci tests are realized by building help seeker: i'm feeling really anxious these days. i'm finding the covid online learning experience to be too much for me at this time. u 3 i want to stop school, but i don't think i can afford to. i need to get done with school. supporter: i understand your frustration. all of us are challenged due to covid. u 4 history help seeker: school was always hard. now it's gotten harder. i think a lot of people are stressed. u 5 human supporter: how long are you doing the online school? u 6 blenderbot supporter: you are welcome. i wish you all the best in your future endeavors. take care. u 7 a classifier to infer whether an utterance in the history statistically depends on the response conditioned on its preceding utterance. as there is no training data for such a classifier, we start with manually annotating causal relations on a small portion of public open-domain dialogues. to overcome the scarcity of the training data, we propose a constrained self-training method, coined con-strain, which is able to identify causal relations with high precision and recall. this classifier is applied to filter out utterances in histories, which are not direct causes of responses, before training response generation models. furthermore, the classifier serves as a scoring function to select the most relevant response from all generated candidates. to sum up, our contributions are as follows: ## causal discovery background given a set of random variables, causal discovery from observational data is concerned with discovering causal relations between the random variables. a set of causal relations constitutes a causal graph @xmath0, e}, where a node @xmath1. a change in v i results in a change in v j , but an intervention in v j does not necessarily lead to a change in v i . our work is motivated by constraint-based causal discovery approaches @xcite , which iteratively apply independence and ci tests to infer causal structures. those approaches make the faithfulness assumption that independencies in a distribution imply the structure of the corresponding causal graph. the most commonly used algorithm in this family is the pc algorithm @xcite . it starts with adding undirected edges between two nodes if both of them are dependent by not passing independence tests. then it remove an edge between two nodes if they are identified as conditionally independent after running ci tests. the algorithm would continue with larger conditioning sets until the skeleton of the graph is identified. finally, it orients the edges when possible by using heuristics and identifying the specific structure, v @xmath2, referred to as immorality, as illustrated in figure 2b @xcite . in this work, we do not need to recover the complete causal structure between utterances in dialogues. instead, we only focus on identifying direct causes of responses, namely, the parents of the response nodes in a causal graph. a causal graph satisfies the causal markov condition, which states that each variable is independent of all its non-descendants, given its parents in the causal graph. hence the value of a response variable is only determined by its parents @xcite . under the faithfulness assumption, if a response variable v j is dependent on v i conditioning on arbitrary any other nodes, and we know the influence direction is from v i to v j , then we conclude that v @xmath3. ## spurious correlations in dialogues the slogan ''spurious correlation is no proof of causation'' is well known in statistics @xcite . a correlation between a response and an utterance in a conversational history is spurious if it does not directly result in the response. spurious correlations are an inherent problem of statistical machine learning (ml) models. @xcite point out that ml models relying on core features may well achieve similar training errors on the same training data as those relying on spurious features. however, the models relying on spurious correlations lead to high test errors because spurious correlations are inconsistent across datasets. overparameterization further exacerbates spurious correlations by memorizing examples containing spurious features @xcite . unfortunately, almost all the sota open-domain dialogue models are based on large-scale transformers, which are overparameterized with respect to small dialogue training datasets in target domains. to study the impact of spurious correlations for dialogue models, we leverage two public dialogue corpora (esconv and msc) to construct a small evaluation corpus for causal graphs in dialogues, coined cgdialog, and evaluate two sota dialogue models, blenderbot @xcite and dialoggpt @xcite , on that corpus in terms of spurious correlations. ## causal discovery motivated training and inference as shown by our empirical study, spurious correlations are detrimental to the sota dialogue models. to remedy this, we propose to automatically identify the utterances in conversation histories, which serve as direct causes to responses, and only use them as history representations during both training and inference. based on the theoretical analysis in section 2, this identification problem is reduced to running ci tests between responses and utterances in their history. herein, we propose a constrained self-training procedure to build a classifier for classifier-based ci tests (lopez-paz and oquab 2017; @xcite @xcite . formally, given a conversation history c @xmath4, . . . , u t-1 } at time t, a dialogue model aims to produce a word sequence r t as the response based on c t . both u i and r t are regarded as collections of random variable, where each variable in the collection denotes if a single word is present or not. because the same event can be expressed in various linguistic forms, we assume there is a projection function g(u), which maps an utterance to a latent random variable vector @xmath5. a causal graph in the semantic space is a directed acyclic graph @xmath6, e}, where a node represents a latent random variable vector z i and an edge is denoted by a causal relation between a pair of nodes. we do not define causal graphs in the word space because i) it is the meanings of utterances that are causally correlated and ii) the same words in different contexts may be involved in different causal relations. identifying direct causes of responses can thus be regarded as recognizing causal relations between those latent random variables. to simplify notation, we denote the output of g(u i ) by z i , unless stated otherwise. ## conclusion we conduct the first study from a causal view to investigate and tackle spurious correlations in dialogues. inspired by constraint-based causal discovery algorithms, we propose a novel constrained self-training method to build a ci classifier by using a small corpus cgdialog, which is manually annotated with causal graphs by us. the ci classifier is applied to filter out spuriously correlated utterances in conversation histories before training a response generation model. that classifier also serves as a scoring function during inference to select the best response from all generated candidates. by identifying conditionally dependencies between utterances and responses, our model agnostic approach significantly improves the overall generation quality of response models in terms of relevance, informativeness and fluency.