Assessing Word Importance Using Models Trained for Semantic Tasks

Many NLP tasks require to automatically identify the most significant words in a text. In this work, we derive word significance from models trained to solve semantic task: Natural Language Inference and Paraphrase Identification. Using an attribution method aimed to explain the predictions of these models, we derive importance scores for each input token. We evaluate their relevance using a so-called cross-task evaluation: Analyzing the performance of one model on an input masked according to the other model's weight, we show that our method is robust with respect to the choice of the initial task. Additionally, we investigate the scores from the syntax point of view and observe interesting patterns, e.g. words closer to the root of a syntactic tree receive higher importance scores. Altogether, these observations suggest that our method can be used to identify important words in sentences without any explicit word importance labeling in training.

DialogueScript: Using Dialogue Agents to Produce a Script

We present a novel approach to generating scripts by using agents with different personality types. To manage character interaction in the script, we employ simulated dramatic networks. Automatic and human evaluation on multiple criteria shows that our approach outperforms a vanilla-GPT2-based baseline. We further introduce a new metric to evaluate dialogue consistency based on natural language inference and demonstrate its validity.

Comprehension of Subtitles from Re-Translating Simultaneous Speech Translation

In simultaneous speech translation, one can vary the size of the output window, system latency and sometimes the allowed level of rewriting. The effect of these properties on readability and comprehensibility has not been tested with modern neural translation systems. In this work, we propose an evaluation method and investigate the effects on comprehension and user preferences. It is a pilot study with 14 users on 2 hours of German documentaries or speeches with online translations into Czech. We collect continuous feedback and answers on factual questions. Our results show that the subtitling layout or flicker have a little effect on comprehension, in contrast to machine translation itself and individual competence. Other results show that users with a limited knowledge of the source language have different preferences to stability and latency than the users with zero knowledge. The results are statistically insignificant, however, we show that our method works and can be reproduced in larger volume.