Felix Hieber

Improved answer ranking in social question-answering portals

Community QA portals provide an important resource for non-factoid question-answering. The inherent noisiness of user-generated data makes the identification of high-quality content challenging but all the more important. We present an approach to answer ranking and show the usefulness of features that explicitly model answer quality. Furthermore, we introduce the idea of leveraging snippets of web search results for query expansion in answer ranking. We present an evaluation setup that avoids spurious results reported in earlier work. Our results show the usefulness of our features and query expansion techniques, and point to the importance of regularization when learning from noisy data.

Learning Translational and Knowledge-based Similarities from Relevance Rankings for Cross-Language Retrieval

We present an approach to cross-language retrieval that combines dense knowledgebased features and sparse word translations. Both feature types are learned directly from relevance rankings of bilingual documents in a pairwise ranking framework. In large-scale experiments for patent prior art search and cross-lingual retrieval in Wikipedia, our approach yields considerable improvements over learningto-rank with either only dense or only sparse features, and over very competitive baselines that combine state-of-the-art machine translation and retrieval.

Learning to translate queries for CLIR

The statistical machine translation (SMT) component of cross-lingual information retrieval (CLIR) systems is often regarded as black box that is optimized for translation quality independent from the retrieval task. In recent work [10], SMT has been tuned for retrieval by training a reranker on

Bag-of-Words Forced Decoding for Cross-Lingual Information Retrieval

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Twitter Translation using Translation-Based Cross-Lingual Retrieval

-best translations ordered according to their retrieval performance. In this paper we propose a decomposable proxy for retrieval quality that obviates the need for costly intermediate retrieval. Furthermore, we explore the full search space of the SMT decoder by directly optimizing decoder parameters under a retrieval-based objective. Experimental results for patent retrieval show our approach to be a promising alternative to the standard pipeline approach.

Boosting Cross-Language Retrieval by Learning Bilingual Phrase Associations from Relevance Rankings

Current approaches to cross-lingual information retrieval (CLIR) rely on standard retrieval models into which query translations by statistical machine translation (SMT) are integrated at varying degree. In this paper, we present an attempt to turn this situation on its head: Instead of the retrieval aspect, we emphasize the translation component in CLIR. We perform search by using an SMT decoder in forced decoding mode to produce a bag-ofwords representation of the target documents to be ranked. The SMT model is extended by retrieval-specific features that are optimized jointly with standard translation features for a ranking objective. We find significant gains over the state-of-the-art in a large-scale evaluation on cross-lingual search in the domains patents and Wikipedia.