BLEU is the de facto standard machine translation (MT) evaluation metric. However, because BLEU computes a geometric mean of n-gram precisions, it often correlates poorly with human judgment on the sentence-level. Therefore, several smoothing techniques have been proposed. This paper systematically compares 7 smoothing techniques for sentence-level BLEU. Three of them are first proposed in this...

The emergence of transformer models like BERT means that deep learning language can achieve reasonably good performance in document classification with few labelled instances. However, there is a lack evidence for the utility applying BERT-like on long few-shot scenarios. This paper introduces long-text-specific model—the Hierarchical Model (HBM)—that learns sentence-level features and works we...

The expression and perception of human emotions are not uniformly distributed over time. Therefore, tracking local changes emotion within a segment can lead to better models for speech recognition (SER), even when the task is provide sentence-level prediction emotional content. A challenge expl...

Measuring the similarity between text fragments at the sentence level is made difficult by the fact that two sentences that are semantically related may not contain any words in common. This means that standard IR measures of text similarity, which are based on word co-occurrence and designed to operate at the document level, are not appropriate. While various sentence similarity measures have ...

Earlier studies have raised the possibility of summarizing at the level of the sentence. This simplification should help in adapting textual content in a limited space. Therefore, sentence compression is an important resource for automatic summarization systems. However, there are few studies that consider sentence-level discourse segmentation for compression task; to our knowledge, none in Spa...

Many automatic evaluation metrics for machine translation (MT) rely on making comparisons to human translations, a resource that may not always be available. We present a method for developing sentence-level MT evaluation metrics that do not directly rely on human reference translations. Our metrics are developed using regression learning and are based on a set of weaker indicators of fluency a...

The opinion analysis task is a pilot study task in NTCIR-6. It contains the challenges of opinion sentence extraction, opinion polarity judgment, opinion holder extraction and relevance sentence extraction. The three former are new tasks, and the latter is proven to be tough in TREC. In this paper, we introduce our system for analyzing opinionated information. Several formulae are proposed to d...