We hope that an understanding of the systematic differences among these human datasets will ultimately allow them to be leveraged more effectively in the associated engineering tasks.
Using n-best candidates generated by a baseline MT model with beam search and we select the candidate that minimizes the absolute difference between the sentiment score of the source sentence and that of the translation and and perform two human evaluations to assess the produced translations.
This paper focuses on a referring expression generation (REG) task in which the aim is to pick out an object in a complex visual scene.
While popular televised events such as presidential debates or TV shows are airing, people provide commentary on them in real-time.
We show that State-of-the-art QE models, when tested in a Parallel Corpus Mining (PCM) setting, perform unexpectedly bad due to a lack of robustness to out-of-domain examples.
Vision-and-Language Navigation (VLN) is a challenging task in the field of artificial intelligence.
We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and propose a training curriculum fit for cases when both parallel training data and compute resource are lacking, reflecting the reality of most of the world's languages and the researchers working on these languages.
The degree of user involvement is flexible: they can run models that have been pre-trained on select issues; submit labeled documents and train a new model for frame classification; or submit unlabeled documents and obtain potential frames of the documents.
Current multilingual vision-language models either require a large number of additional parameters for each supported language, or suffer performance degradation as languages are added.
Our method extracts ngrams that capture a persons speaking styles and uses the ngrams to create patterns for transforming sentences to the persons speaking styles.
Hand-built verb clusters such as the widely used Levin classes (Levin, 1993) have proved useful, but have limited coverage.