Zero-Shot Rationalization by Multi-Task Transfer Learning from Question Answering (2020)

Abstract: Extracting rationales can help human understand which information the model utilizes and how it makes the prediction towards better interpretability. However, annotating rationales requires much effort and only few datasets contain such labeled rationales, making supervised learning for rationalization difficult. In this paper, we propose a novel approach that leverages the benefits of both multi-task learning and transfer learning for generating rationales through question answering in a zero-shot fashion. For two benchmark rationalization datasets, the proposed method achieves comparable or even better performance of rationalization without any supervised signal, demonstrating the great potential of zero-shot rationalization for better interpretability.

## introduction resolving nlp tasks by deep neural networks has been proven to be effective, and it is also important to investigate how the models make such a decision. for example, only providing the prediction to medical tasks may not be enough, and providing the associated reasons is more crucial for the practical applications. therefore, there has been increasing attempts that focus on interpretability or explainability of the machine-learned models. there are different ways of explaining how machines make the decision @xcite @xcite , and one of these methods is to extract rationales @xcite . however, most prior work focused on extracting rationales in a supervised manner @xcite , but not all datasets contain such annotated rationales for model learning, making the rationalization task difficult and impractical. rationalization is defined as a task that focuses on extracting the rationales from the input texts for better justification and interpretation. @xcite is the first work that attempted to extract rationales in order to justify the model's answers, where a rationale generator extracts the context and a predictor generates the answer based on the extracted rationales. this method shows great precision in extracting rationales. recently, @xcite proposed an introspective model, an extension of the prior work that further improved the comprehensiveness of the extracted rationales. @xcite proposed to learn rationale extraction in a supervised manner and prepared the benchmark experiments in diverse rationalization tasks. from the experimental results, it can be found that supervised learning for rationalization may not be always better than the unsupervised method due to the complex reasoning process. considering that in the practical application, the target domain may not contain the annotated rationales for supervised training, transferring the knowledge about rationalization to the target domain may be applicable. @xcite proposed to utilize the pre-trained language model for explaining the common sense towards zero-shot knowledge transfer. however, it requires that the target domain should be covered by the pre-trained language model so that the common sense questions can be well-answered. such requirements limit the potential of being applied to a lot of realworld applications, because the target domain we aim at extracting rationales for may not be general (e.g. medical texts and financial texts may not be covered by the pre-trained model). instead of directly transferring the knowledge to the target domain, this paper proposes to borrow the benefit of multi-task learning, which allows a single model to be capable of handling multiple tasks/domains by feeding the corresponding data. specifically, we utilize question answering for learning the capability of rationalization instead of a rationalizationspecific language model. we can use the data (with the labels different from rationales) from one domain to feed into our multi-task model such that the qa-part of our model is capable of performing rationalization on the target data from other domains. to enable multi-task transfer learning towards zero-shot rationalization, focusing on gaining the insight into data and simultaneously maintaining the capability of generalization is not trivial. multitask learning can address the issue about lack of training data in certain domains and alleviate overfitting through regularization effect @xcite . furthermore, @xcite showed that training on different tasks by turns in every batch can significantly boost the regularization effect towards better generalization. following the prior success, this paper focuses on extracting rationales via the capability of qa and handling other tasks with the target data at the same time; with multi-task learning for enhancing the capability of generalization, the model can handle diverse questions (including rationalization) from diverse domains. this paper has three-fold contributions: ## datasets this paper focuses on zero-shot rationalization by transferring knowledge from question answering. thus, three datasets are used in the experiments, where a qa dataset, squad 2.0, is utilized for the transfer purpose and two benchmark rationalization datasets, beerreview and moviereview, are used for evaluating the performance of zero-shot rationalization for the proposed method. the datasets are briefly introduced below, and their statistics is detailed in squad 2.0 stanford question answering dataset (squad) @xcite is a benchmark dataset for reading comprehension, consisting of questions posed by crowdworkers on over 500 wikipedia articles. the answer to each question is a text span from the corresponding paragraph. there are 100,000 answerable questions and over 50,000 unanswerable questions similar to answerable ones written by crowdworkers adversarially. this data is for enhancing the ability of text understanding in our model so that we can transfer the knowledge to zero-shot rationalization. beerreview this is a beer review dataset processed by @xcite foot_1 , which contains 1.5 million reviews written by the website users. the reviews have the associated multi-aspect ratings from 0 to 1: appearance, aroma, palate, taste, and overall rating in order. we randomly sample 30,000 reviews as our training set shown in moviereview this dataset contains the reviews obtained from imdb, where each review was labeled as positive or negative without any rationales, because it is originally proposed for sentiment analysis @xcite . another similar dataset consists of 2,000 movie reviews from imdb with their rationales that explain why the review is positive/negative @xcite . note that each review may contain multiple rationales. hence, we similarly utilize the annotated rationales as the testing data for validating the performance of zero-shot rationalization. ## proposed approach in order to perform zero-shot rationalization, we leverage the question-answering ability for finding the rationales in a given document that may come from an unseen domain. here we propose an encoder-predictor model with multi-task learning illustrated in figure 1 , where the weights of the encoder are shared across different tasks (qa, beer rating classification, and movie rating classification). the multi-task learning model is to learn good representations of the inputs from different domains. to prevent the encoder from identifying the task type according to the input format, we add an additional question after each review as the new input, so that all inputs of three tasks are in the context-question format. in addition, there is a task-specific predictor added after the encoder for each distinct task, so they would not intervene with one another while training. during training, we fine-tune the pre-trained model for three tasks at the same time as illustrated in figure 2 . during testing, given a context from any domain with a corresponding question, the question-answering module (the right branch) is capable of finding the associated rationale we expect without training on the rationales from the target domain (highlighted in red in figure 2 ), achieving zero-shot rationalization. ## experiments in our rationalization task, we compare the performance with the prior work in terms of precision and recall on beerreview and token f1 and iou f1 on moviereview. 3 furthermore, we show the f1 scores of our model on beerreview data for future benchmark comparison. ## discussion to better understand the limits and potential of our proposed method, we further study about the qa ability and comprehensiveness of our model. ## conclusion in this paper, we propose a novel framework that leverages multi-task learning for zero-shot task transfer, where the question answering model is utilized to perform rationalization for diverse domains. by training on multiple tasks alternately, we improve the universal understanding of the context and is able to use the qa structure to extract rationales from any tasks/data. the experiments of benchmark rating prediction datasets for two domains are conducted, and the results show that our proposed model achieves comparable or better performance of rationalization compared with the prior work and meanwhile preserves better capability of generalization and flexibility towards better interpretability.