At the June 22nd lab meeting (11:30am), Anahita will talk about Joint Prediction of Word Alignment with Alignment Types.
Abstract: Probabilistic models for word alignment do not distinguish between different types of word alignment links. We provide a new probabilistic model for word alignment in which alignments are associated with linguistically motivated alignment types. We provide a novel task of joint prediction of word alignment and alignment types.
On May 25th 11:30am, Andrei will give a presentation on the CKY algorithm and Canny, Hall, and Klein’s 2013 paper “A Multi-Teraflop Constituency Parser using GPUs.”
Several lab members will be traveling to the North West NLP Workshop NW-NLP 2016 in Seattle, USA on Friday May 20th.
The workshop is hosted by Amazon, Inc. at Suite 700, 1220 Howell Street, Seattle WA.
The following papers from the SFU Natlang Lab will be presented there:
Also presenting from SFU Linguistics:
On May 18th 11:30am, Jasneet will give a practice talk for his Masters thesis defence on the topic “BILINGUAL LANGUAGE MODELS USING WORD EMBEDDINGS FOR MACHINE TRANSLATION”.
On May 19th 2pm in TASC1 9204, Jasneet will defend his Masters thesis on the topic “BILINGUAL LANGUAGE MODELS USING WORD EMBEDDINGS FOR MACHINE TRANSLATION”.
Bilingual language models (Bi-LMs) refer to language models over pairs of words in source and target languages in a machine translation task. When translating from source to target language, the decoder in phrase-based machine translation system segments the source sentence into phrases and then translates each phrase to the target language. While decoding each phrase, the decoder does not have sufficient information about source words that are outside the phrase under consideration. Bi-LMs have been used to tackle this problem. Bi-LMs are estimated by first creating bi-token sequences using word alignments over a parallel corpus. We propose the use of bilingual word embeddings to deal with the large number of bi-token types in a bi-token language model. Our approach outperforms previous work with an increase of 1.4 BLEU points in our machine translation experiments.
M.Sc. Examining Committee: