Subjective Question Answering: Deciphering the inner workings of Transformers in the realm of subjectivity

• URL: http://arxiv.org/abs/2006.08342v2
• Date: Wed, 14 Oct 2020 07:47:52 GMT
• Title: Subjective Question Answering: Deciphering the inner workings of Transformers in the realm of subjectivity
• Authors: Lukas Muttenthaler
• Abstract summary: I've exploited a recently released dataset for span-selection Question Answering, namely SubjQA. SubjQA is the first dataset that contains questions that ask for subjective opinions corresponding to review paragraphs from six different domains. I've investigated the inner workings of a Transformer-based architecture to contribute to a better understanding of these not yet well understood "black-box" models.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding subjectivity demands reasoning skills beyond the realm of common knowledge. It requires a machine learning model to process sentiment and to perform opinion mining. In this work, I've exploited a recently released dataset for span-selection Question Answering, namely SubjQA. SubjQA is the first QA dataset that contains questions that ask for subjective opinions corresponding to review paragraphs from six different domains. Hence, to answer these subjective questions, a learner must extract opinions and process sentiment for various domains, and additionally, align the knowledge extracted from a paragraph with the natural language utterances in the corresponding question, which together enhance the difficulty of a QA task. The primary goal of this thesis was to investigate the inner workings (i.e., latent representations) of a Transformer-based architecture to contribute to a better understanding of these not yet well understood "black-box" models. Transformer's hidden representations, concerning the true answer span, are clustered more closely in vector space than those representations corresponding to erroneous predictions. This observation holds across the top three Transformer layers for both objective and subjective questions and generally increases as a function of layer dimensions. Moreover, the probability to achieve a high cosine similarity among hidden representations in latent space concerning the true answer span tokens is significantly higher for correct compared to incorrect answer span predictions. These results have decisive implications for down-stream applications, where it is crucial to know about why a neural network made mistakes, and in which point, in space and time the mistake has happened (e.g., to automatically predict correctness of an answer span prediction without the necessity of labeled data).

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