Gonçalo Simões


2020

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Text Segmentation by Cross Segment Attention
Michal Lukasik | Boris Dadachev | Kishore Papineni | Gonçalo Simões
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Document and discourse segmentation are two fundamental NLP tasks pertaining to breaking up text into constituents, which are commonly used to help downstream tasks such as information retrieval or text summarization. In this work, we propose three transformer-based architectures and provide comprehensive comparisons with previously proposed approaches on three standard datasets. We establish a new state-of-the-art, reducing in particular the error rates by a large margin in all cases. We further analyze model sizes and find that we can build models with many fewer parameters while keeping good performance, thus facilitating real-world applications.

2018

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Morphosyntactic Tagging with a Meta-BiLSTM Model over Context Sensitive Token Encodings
Bernd Bohnet | Ryan McDonald | Gonçalo Simões | Daniel Andor | Emily Pitler | Joshua Maynez
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

The rise of neural networks, and particularly recurrent neural networks, has produced significant advances in part-of-speech tagging accuracy. One characteristic common among these models is the presence of rich initial word encodings. These encodings typically are composed of a recurrent character-based representation with dynamically and pre-trained word embeddings. However, these encodings do not consider a context wider than a single word and it is only through subsequent recurrent layers that word or sub-word information interacts. In this paper, we investigate models that use recurrent neural networks with sentence-level context for initial character and word-based representations. In particular we show that optimal results are obtained by integrating these context sensitive representations through synchronized training with a meta-model that learns to combine their states.