|
{ |
|
"paper_id": "2005", |
|
"header": { |
|
"generated_with": "S2ORC 1.0.0", |
|
"date_generated": "2023-01-19T07:21:18.556529Z" |
|
}, |
|
"title": "Log-Linear Model Approach to SMT Maximum Entropy framework for the word-alignment MT approach", |
|
"authors": [], |
|
"year": "", |
|
"venue": null, |
|
"identifiers": {}, |
|
"abstract": "", |
|
"pdf_parse": { |
|
"paper_id": "2005", |
|
"_pdf_hash": "", |
|
"abstract": [], |
|
"body_text": [ |
|
{ |
|
"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. ", |
|
"cite_spans": [], |
|
"ref_spans": [ |
|
{ |
|
"start": 35, |
|
"end": 42, |
|
"text": ", 2003)", |
|
"ref_id": null |
|
} |
|
], |
|
"eq_spans": [], |
|
"section": "", |
|
"sec_num": null |
|
}, |
|
{ |
|
"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", |
|
"cite_spans": [], |
|
"ref_spans": [], |
|
"eq_spans": [], |
|
"section": "", |
|
"sec_num": null |
|
}, |
|
{ |
|
"text": "Pittsburgh, 24-25 October 2005", |
|
"cite_spans": [], |
|
"ref_spans": [], |
|
"eq_spans": [], |
|
"section": "", |
|
"sec_num": null |
|
}, |
|
{ |
|
"text": "M. Federico, ITC-irst IWSLT 2005Pittsburgh, 24-25 October 2005", |
|
"cite_spans": [], |
|
"ref_spans": [], |
|
"eq_spans": [], |
|
"section": "", |
|
"sec_num": null |
|
} |
|
], |
|
"back_matter": [ |
|
{ |
|
"text": "In this real example, the CLA alignment allows to extract the useful phrase \"where is\".", |
|
"cite_spans": [], |
|
"ref_spans": [], |
|
"eq_spans": [], |
|
"section": "Phrase extraction from IBM and CLA alignments", |
|
"sec_num": null |
|
}, |
|
{ |
|
"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. ", |
|
"cite_spans": [], |
|
"ref_spans": [ |
|
{ |
|
"start": 75, |
|
"end": 93, |
|
"text": "-grams (n=1,2,3,4)", |
|
"ref_id": null |
|
} |
|
], |
|
"eq_spans": [], |
|
"section": "New Feature Functions in Re-scoring", |
|
"sec_num": null |
|
} |
|
], |
|
"bib_entries": {}, |
|
"ref_entries": { |
|
"FIGREF1": { |
|
"text": "", |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null |
|
}, |
|
"FIGREF2": { |
|
"text": "", |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null |
|
}, |
|
"FIGREF3": { |
|
"text": "", |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null |
|
}, |
|
"FIGREF5": { |
|
"text": "", |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null |
|
}, |
|
"FIGREF6": { |
|
"text": "", |
|
"type_str": "figure", |
|
"num": null, |
|
"uris": null |
|
}, |
|
"TABREF0": { |
|
"text": "", |
|
"html": null, |
|
"type_str": "table", |
|
"num": null, |
|
"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>" |
|
} |
|
} |
|
} |
|
} |