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Natural Language Processing (NLP) group

The Idiap NLP group studies how semantic and discourse processing of texts and dialogues can improve statistical machine translation and information indexing, specifically multimedia retrieval and recommendation.

In statistical machine translation (SMT), we have shown that automatic labeling of discourse-level information (such as the senses of discourse connectives or the tense/aspects of verbs) can be combined with phrase-based SMT systems to improve their accuracy. 

In multimedia retrieval, we have developed a system for just-in-time recommendation of documents in conversations, and showed that topic-aware keyword extraction and semantic search improve its accuracy.  Moreover, we have proposed methods for multimedia recommendation, applied to lectures, TV shows, and their segments, and showed that leveraging user comments can improve collaborative filtering.  Moreover, searching over networked data was improved by using linguistic information and information from the network itself.

Current Group Members

Group Members

Andrei Popescu-Belis
Lesly Miculicich
Nikolaos Pappas
Ngoc Quang Luong
Xiao Pu

Alumni


Quoc Anh Le
Chidansh Bhatt
Catherine Gasnier
Maryam Habibi
Najeh Hajlaoui
Pierre Lison
Jeevanthi Liyanapathirana
Sharid Loaiciga
Parvaz Mahdabi
Lukas Matena
Braida Meyer
Thomas Meyer
Majid Yazdani

Current projects

NMTBENCHMARK - Name Training and Benchmarking Neural MT and ASR Systems for Swiss Languages
This document is a proposal for work on neural machine translation (MT) and automatic speech recognition (ASR) technology by the Idiap Research Institute.

Recent Projects

COMTIS - Improving the coherence of machine translation output by modeling intersentential relations
AROLES - Automatic Recommendation of Lectures and Snipets
MODERN - Modeling Discourse Entities and Relations for Coherent Machine Translation
MODERN is a SNSF Sinergia projected started in August 2013 for a duration of 3 years (grant n. CRSII2_147653). MODERN is led by the Idiap Research Institute with participants from the Universities of Geneva, Utrecht (the Netherlands), and Zurich.
REMUS - REMUS: Re-ranking Multiple Search Results for Just-in-Time Document Recommendation
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