The 2019 BBN Cross-lingual Information Retrieval System
Abstract
In this paper, we describe a cross-lingual information retrieval (CLIR) system that, given a query in English, and a set of audio and text documents in a foreign language, can return a scored list of relevant documents, and present findings in a summary form in English. Foreign audio documents are first transcribed by a state-of-the-art pretrained multilingual speech recognition model that is fine tuned to the target language. For text documents, we use multiple multilingual neural machine translation (MT) models to achieve good translation results, especially for low/medium resource languages. The processed documents and queries are then scored using a probabilistic CLIR model that makes use of the probability of translation from GIZA translation tables and scores from a Neural Network Lexical Translation Model (NNLTM). Additionally, advanced score normalization, combination, and thresholding schemes are employed to maximize the Average Query Weighted Value (AQWV) scores. The CLIR output, together with multiple translation renderings, are selected and translated into English snippets via a summarization model. Our turnkey system is language agnostic and can be quickly trained for a new low-resource language in few days.
Document Details
- Document Type
- Technical Report
- Publication Date
- May 11, 2020
- Accession Number
- AD1149901
Entities
People
- Damianos Karakos
- David Akodes
- John Makhoul
- Le Zhang
- Lee Tarlin
- Lingjun Zhao
- Manaj Srivastava
- Numra Bathool
- Richard Schwartz
- Sanjay Krishna Gouda
- William Hartmann
- Zhuolin Jiang
Organizations
- RTX