Amin Ahmad


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Multilingual Universal Sentence Encoder for Semantic Retrieval
Yinfei Yang | Daniel Cer | Amin Ahmad | Mandy Guo | Jax Law | Noah Constant | Gustavo Hernandez Abrego | Steve Yuan | Chris Tar | Yun-hsuan Sung | Brian Strope | Ray Kurzweil
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

We present easy-to-use retrieval focused multilingual sentence embedding models, made available on TensorFlow Hub. The models embed text from 16 languages into a shared semantic space using a multi-task trained dual-encoder that learns tied cross-lingual representations via translation bridge tasks (Chidambaram et al., 2018). The models achieve a new state-of-the-art in performance on monolingual and cross-lingual semantic retrieval (SR). Competitive performance is obtained on the related tasks of translation pair bitext retrieval (BR) and retrieval question answering (ReQA). On transfer learning tasks, our multilingual embeddings approach, and in some cases exceed, the performance of English only sentence embeddings.


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ReQA: An Evaluation for End-to-End Answer Retrieval Models
Amin Ahmad | Noah Constant | Yinfei Yang | Daniel Cer
Proceedings of the 2nd Workshop on Machine Reading for Question Answering

Popular QA benchmarks like SQuAD have driven progress on the task of identifying answer spans within a specific passage, with models now surpassing human performance. However, retrieving relevant answers from a huge corpus of documents is still a challenging problem, and places different requirements on the model architecture. There is growing interest in developing scalable answer retrieval models trained end-to-end, bypassing the typical document retrieval step. In this paper, we introduce Retrieval Question-Answering (ReQA), a benchmark for evaluating large-scale sentence-level answer retrieval models. We establish baselines using both neural encoding models as well as classical information retrieval techniques. We release our evaluation code to encourage further work on this challenging task.