| LI MICHAEL WENTAO | M, 2回目発表 | 自然言語処理学 | 松本 裕治, 中村 哲, 新保 仁, 進藤 裕之 |
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title: Semantic Sentence Similarity for Machine Translation Evaluation
abstract: The automatic evaluation of machine translation, which may have arbitrarily many correct outputs for a given input, is a challenging task. We define a good translation as a sentence with the most similar meaning to the source sentence, and explore various semantic sentence similarity techniques to measure that similarity. Due to the lack of suitable training data for many of these techniques, we also investigate various methods of data augmentation, to encourage our evaluation systems to make fine-grained distinctions between translations of similar quality. Our results showed that while this approach has promise, it is still difficult to obtain a good correlation with human evaluation. language of the presentation: English | |||
| LU YUXUN | M, 2回目発表 | 自然言語処理学 | 松本 裕治, 中村 哲, 新保 仁, 進藤 裕之 |
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title: Reduce Hubness in Knowledge Graph Embedding Models.
abstract: Knowledge graph embedding models provide a simplified way to manipulating knowledge graphs while preserving their inherent structures by embedding the symbolic entities and relations in knowledge graphs to continuous vector space. But due to the intrinsic property of high dimensional data, the hubness phenomenon occurs and it can affect the performance of knowledge graph embedding models whose results of query (h,r,?) or (?,r,t) are given by ranking the distance between all entities' embeddings and the transfered query embedding f(h,r) or f(r,t) descendingly. This presentation would introduce our investigation regarding to analyzing hubness with a typical translation-based model, TransE, and our attempt to relieve the problem. language of the presentation: English | |||