---
title: "Statistical Machine Translation for Query Expansion in Answer"
description: "The paper tackles the vocabulary mismatch between user questions and the answers that… 90% F1. 25 references, cited by 78 papers here."
authors: ["Stefan Riezler", "Alexander Vasserman", "Ioannis Tsochantaridis", "Vibhu Mittal", "Yi Liu"]
year: 2007
venue: "Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics"
source: "https://aclanthology.org/P07-1059/"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/riezler-2007-statistical-machine-translation-query-expansion/"
---
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# Statistical Machine Translation for Query Expansion in Answer Retrieval

**Authors:** [Stefan Riezler](https://md.research.elamur.ai/free/en/authors/stefan-riezler/), Alexander Vasserman, Ioannis Tsochantaridis, [Vibhu Mittal](https://md.research.elamur.ai/free/en/authors/vibhu-mittal/) and [Yi Liu](https://md.research.elamur.ai/free/en/authors/yi-liu/) · **Year:** 2007 · **Venue:** [Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2007](https://md.research.elamur.ai/free/en/venues/acl/2007/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P07-1059/) · [PDF](https://aclanthology.org/P07-1059.pdf) · [OpenAlex](https://openalex.org/W2105051853)

**Citations:** 213 citations (OpenAlex, 2026-10-14) · [78](https://md.research.elamur.ai/free/en/papers/riezler-2007-statistical-machine-translation-query-expansion/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
The paper tackles the vocabulary mismatch between user questions and the answers that would satisfy them, focusing on answer retrieval from FAQ pages. The authors treat this as a query expansion problem and solve it with phrase-based SMT. They use two expansion strategies. The first is a question-to-answer "translation" model trained on roughly 10 million question-answer pairs mined from FAQ pages. The second is a full-sentence paraphraser whose phrase table is derived from bilingual phrase tables (following Bannard and Callison-Burch) and refined with discriminative training on manually created paraphrases. In both cases, new content words are taken from the n-best outputs for the whole query, so a large n-gram language model helps resolve ambiguity in context. The expanded queries are run through the tfidf answer retrieval setup of Jijkoun and de Rijke (2005). Compared with that unexpanded baseline and with local pseudo-relevance-feedback expansion (Xu and Croft), SMT-based expansion clearly raises the share of queries that get an adequate answer. This shows that MT machinery can serve as a context-aware, corpus-wide source of expansion terms.

## Key points
- **Corpus construction:**
  - From a 4-billion-page web subset, URL matching on "faq"/"faqs" yielded 2.6M pages (0.07% of the crawl).
  - A passive-aggressive classifier with 20 features, trained on 1,000 hand-labelled pages, reached about 90% F1 and kept 795,483 FAQ pages.
- **QA pair extraction:**
  - Extraction produced 10,568,160 QA pairs, about 13.3 per FAQ page.
  - Manual checking of 100 documents (1,303 pairs) showed 98% precision and 82% recall.
- **QA translation model setup:**
  - There was no discriminative weight tuning; 4,000 held-out pairs were used for manual development and testing instead.
  - The null-word alignment probability was set to 0.9 to handle the length gap between questions and answers.
  - Phrases were extracted from the intersection of alignments in both directions.
- **Evaluation design:**
  - Two judges rated the top 20 results for 60 queries on a 0/1/2 scale (unsatisfactory / material / adequate).
  - Results were reported with success rates S2@n and S1,2@n rather than MRR.
- **Results (relative gains in S2 over the tfidf baseline):**
  - At top 10, SMT-based expansion gained 40.7%, versus 11.1% for local expansion.
  - At top 20, the gains were 22.8% versus 14.2%.
  - On S1,2, SMT-based expansion stayed level, while local expansion dropped slightly.

## Interesting ideas
- **Questions and answers as two "languages":** A standard parallel-corpus MT pipeline can then learn lexical bridges between them, without aiming for fluent output. The same trick could connect any paired texts with different vocabularies, such as queries and product descriptions, or colloquial and official place names.
- **Expanding from n-best outputs of the whole query:** Taking new terms from full-sentence translations, rather than expanding word by word, lets the language model filter out senses that do not fit the context. This is useful wherever thesaurus-style expansion introduces noise from ambiguous words.
- **Bilingual phrase tables as a synonym source:** Pivoting through another language turns existing bilingual phrase tables into a high-coverage paraphrase resource. This is attractive for languages with weak monolingual thesauri but available parallel data.
- **Precision-first mining of web data:** A small hand-labelled sample trains a light classifier, followed by cue-based field extraction with label propagation. The result is cleaner training data at some cost in recall, a good trade-off when the data feeds model training.
- **Coverage-oriented evaluation with reusable judgments:** - Success-rate metrics reward systems that find answers for more queries, which MRR may not do in low-recall settings. - Storing judge ratings by item ID, and reconciling disagreements between the two judges, keeps assessments consistent across systems. - The stored ratings also cut the manual labelling effort for each new system.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Stefan Riezler, Alexander Vasserman, Ioannis Tsochantaridis, Vibhu Mittal and Yi Liu. 2007. Statistical Machine Translation for Query Expansion in Answer Retrieval. Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{riezler2007statistical,
  title = {{Statistical Machine Translation for Query Expansion in Answer Retrieval}},
  author = {Stefan Riezler, Alexander Vasserman, Ioannis Tsochantaridis, Vibhu Mittal and Yi Liu},
  booktitle = {Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics},
  year = {2007},
  url = {https://aclanthology.org/P07-1059/}
}
```

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*Updated 24 October 2026*