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{
"paper_id": "2005",
"header": {
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"date_generated": "2023-01-19T07:21:18.556529Z"
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"title": "Log-Linear Model Approach to SMT Maximum Entropy framework for the word-alignment MT approach",
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"text": "Search is over strings of phrases: , 2003) showed that quality of CLA alignments is poorer than for IBM Model 1, we found that such alignments work indeed well for phrase-based SMT. ",
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"start": 35,
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"text": ", 2003)",
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{
"text": ") 2 \u00a1 \u00a3 \u00a4 \u00a5 \u00a6 \u00a7 \u00a4 \u00a9 3 \u00a7 \u00a4 \u00a9 ! \" # \" % $ \" ) & 1 e 0 e ~2 \u1ebd 3 \u1ebd 4 \u1ebd f 1 f 4 f 6 f 2 f 3 f",
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{
"text": "Pittsburgh, 24-25 October 2005",
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{
"text": "M. Federico, ITC-irst IWSLT 2005Pittsburgh, 24-25 October 2005",
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"back_matter": [
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"text": "In this real example, the CLA alignment allows to extract the useful phrase \"where is\".",
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"section": "Phrase extraction from IBM and CLA alignments",
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},
{
"text": "The following statistics are computed on each entry of the 1000-best list: -grams (n=1,2,3,4) within the full n-best list and sums them up according to a linear combination. ",
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{
"start": 75,
"end": 93,
"text": "-grams (n=1,2,3,4)",
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"section": "New Feature Functions in Re-scoring",
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],
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"content": "<table><tr><td>Two</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>e 1</td><td>e 2</td><td>3</td><td>e 4</td><td>e 5</td><td>e 6</td><td>e 7</td><td>words target</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>3</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>phrases target</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>2</td></tr><tr><td>4</td><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td colspan=\"3\">f ~2 f \u1ebd</td><td/><td/><td colspan=\"2\">phrases source 1 3 f</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>1</td></tr><tr><td/><td/><td/><td/><td/><td>5</td><td/><td>words source</td></tr></table>"
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