145
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ACL
2012
13 years 6 months ago
2012
In this paper, we develop an RST-style textlevel discourse parser, based on the HILDA discourse parser (Hernault et al., 2010b). We significantly improve its tree-building step b...
129
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ACL
2012
13 years 6 months ago
2012
Since we can ‘spin’ words and concepts to suit our affective needs, context is a major determinant of the perceived affect of a word or concept. We view this re-profiling as a...
148
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ACL
2012
13 years 6 months ago
2012
Learning for sentence re-writing is a fundamental task in natural language processing and information retrieval. In this paper, we propose a new class of kernel functions, referre...
142
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ACL
2012
13 years 6 months ago
2012
Previous approaches to instruction interpretation have required either extensive domain adaptation or manually annotated corpora. This paper presents a novel approach to instructi...
147
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ACL
2012
13 years 6 months ago
2012
In this paper, we address the issue for learning better translation consensus in machine translation (MT) research, and explore the search of translation consensus from similar, r...
138
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ACL
2012
13 years 6 months ago
2012
We address the problem of learning the mapping between words and their possible pronunciations in terms of sub-word units. Most previous approaches have involved generative modeli...
148
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ACL
2012
13 years 6 months ago
2012
We present a holistic data-driven approach to image description generation, exploiting the vast amount of (noisy) parallel image data and associated natural language descriptions ...
132
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ACL
2012
13 years 6 months ago
2012
We introduce a novel method for grammatical error correction with a number of small corpora. To make the best use of several corpora with different characteristics, we employ a me...
156
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ACL
2012
13 years 6 months ago
2012
In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation (SMT), aiming at increased efficiency of ...
145
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ACL
2012
13 years 6 months ago
2012
This paper presents an unsupervised approach to learning translation span alignments from parallel data that improves syntactic rule extraction by deleting spurious word alignment...