A Neural Approach to Cross-Lingual Information Retrieval

• Main Author: Liu, Qing
• Format: Others
• Published: Research Showcase @ CMU 2018
• Online Access: http://repository.cmu.edu/theses/135; http://repository.cmu.edu/cgi/viewcontent.cgi?article=1141&context=theses

With the rapid growth of world-wide information accessibility, cross-language information retrieval (CLIR) has become a prominent concern for search engines. Traditional CLIR technologies require special purpose components and need high quality translation knowledge (e.g. machine readable dictionaries, machine translation systems) and careful tuning to achieve high ranking performance. However, with the help of a neural network architecture, it’s possible to solve CLIR problem without extra tuning or special components. This work proposes a bilingual training approach, a neural CLIR solution allowing automatic learning of translation relationships from noisy translation knowledge. External sources of translation knowledge are used to generate bilingual training data then the bilingual training data is fed into a kernel based neural ranking model. During the end-to-end training, word embeddings are tuned to preserve translation relationships between bilingual word pairs and also tailored for the ranking task. In experiments we show that the bilingual training approach outperforms traditional CLIR techniques given the same external translation knowledge source and it’s able to yield ranking results as good as that of a monolingual information retrieval system. In experiments we investigate the source of effectiveness for our neural CLIR approach by analyzing the pattern of trained word embeddings. Also, possible methods to further improve performance are explored in experiments, including cleaning training data by removing ambiguous training queries, exploring whether more training data will improve the performance by learning the relationship between training dataset size and model performance, and investigating the affect of English queries’ text-transform in training data. Lastly, we design an experiment that analyzes the quality of testing query translation to quantify the model performance in a real testing scenario where model takes manually written English queries as input.