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Abstract of Aaron Han’s Presentation
The main topic of this presentation will be the “evaluation of machine translation”. With the rapid development of machine translation (MT), the MT evaluation becomes more and more important to tell whether they make some progresses. The traditional human judgments are very time-consuming and expensive. On the other hand, there are some weaknesses in the existing automatic MT evaluation metrics:
– perform well in certain language pairs but weak on others, which we call the language-bias problem;
– consider no linguistic information (leading the metrics result in low correlation with human judgments) or too many linguistic features (difficult in replicability), which we call the extremism problem;
– design incomprehensive factors (e.g. precision only).
To address the existing problems, he has developed several automatic evaluation metrics:
– Design tunable parameters to address the language-bias problem;
– Use concise linguistic features for the linguistic extremism problem;
– Design augmented factors.
The experiments on ACL-WMT corpora show the proposed metrics yield higher correlation with human judgments. The proposed metrics have been published on international top conferences, e.g. COLING and MT SUMMIT. Actually speaking, the evaluation works are very related to the similarity measuring. So these works can be further developed into other literature, such as information retrieval, question and answering, searching, etc.
A brief introduction about some of his other researches will also be mentioned, such as Chinese named entity recognition, word segmentation, and multilingual treebanks, which have been published on Springer LNCS and LNAI series. Precious suggestions and comments are much appreciated. The opportunities of further corporation will be more exciting.
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