Evaluation Discrepancy Discovery: A Sentence Compression Case-study

Reliable evaluation protocols are of utmost importance for reproducible NLP research. In this work, we show that sometimes neither metric nor conventional human evaluation is sufficient to draw conclusions about system performance. Using sentence compression as an example task, we demonstrate how a system can game a well-established dataset to achieve state-of-the-art results. In contrast with the results reported in previous work that showed correlation between human judgements and metric scores, our manual analysis of state-of-the-art system outputs demonstrates that high metric scores may only indicate a better fit to the data, but not better outputs, as perceived by humans.

Empirical Evaluation of Supervision Signals for Style Transfer Models

Text style transfer has gained increasing attention from the research community over the recent years. However, the proposed approaches vary in many ways, which makes it hard to assess the individual contribution of the model components. In style transfer, the most important component is the optimization technique used to guide the learning in the absence of parallel training data. In this work we empirically compare the dominant optimization paradigms which provide supervision signals during training: backtranslation, adversarial training and reinforcement learning. We find that backtranslation has model-specific limitations, which inhibits training style transfer models. Reinforcement learning shows the best performance gains, while adversarial training, despite its popularity, does not offer an advantage over the latter alternative. In this work we also experiment with Minimum Risk Training, a popular technique in the machine translation community, which, to our knowledge, has not been empirically evaluated in the task of style transfer. We fill this research gap and empirically show its efficacy.