Joel Ruben Antony Moniz


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Weakly Supervised Attention Networks for Entity Recognition
Barun Patra | Joel Ruben Antony Moniz
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

The task of entity recognition has traditionally been modelled as a sequence labelling task. However, this usually requires a large amount of fine-grained data annotated at the token level, which in turn can be expensive and cumbersome to obtain. In this work, we aim to circumvent this requirement of word-level annotated data. To achieve this, we propose a novel architecture for entity recognition from a corpus containing weak binary presence/absence labels, which are relatively easier to obtain. We show that our proposed weakly supervised model, trained solely on a multi-label classification task, performs reasonably well on the task of entity recognition, despite not having access to any token-level ground truth data.

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Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
Barun Patra | Joel Ruben Antony Moniz | Sarthak Garg | Matthew R. Gormley | Graham Neubig
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isometry of the two spaces. We propose a technique to quantitatively estimate this assumption of the isometry between two embedding spaces and empirically show that this assumption weakens as the languages in question become increasingly etymologically distant. We then propose Bilingual Lexicon Induction with Semi-Supervision (BLISS) — a semi-supervised approach that relaxes the isometric assumption while leveraging both limited aligned bilingual lexicons and a larger set of unaligned word embeddings, as well as a novel hubness filtering technique. Our proposed method obtains state of the art results on 15 of 18 language pairs on the MUSE dataset, and does particularly well when the embedding spaces don’t appear to be isometric. In addition, we also show that adding supervision stabilizes the learning procedure, and is effective even with minimal supervision.

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Learning to Relate from Captions and Bounding Boxes
Sarthak Garg | Joel Ruben Antony Moniz | Anshu Aviral | Priyatham Bollimpalli
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

In this work, we propose a novel approach that predicts the relationships between various entities in an image in a weakly supervised manner by relying on image captions and object bounding box annotations as the sole source of supervision. Our proposed approach uses a top-down attention mechanism to align entities in captions to objects in the image, and then leverage the syntactic structure of the captions to align the relations. We use these alignments to train a relation classification network, thereby obtaining both grounded captions and dense relationships. We demonstrate the effectiveness of our model on the Visual Genome dataset by achieving a recall@50 of 15% and recall@100 of 25% on the relationships present in the image. We also show that the model successfully predicts relations that are not present in the corresponding captions.