29 research outputs found
Learning Structured Text Representations
In this paper, we focus on learning structure-aware document representations
from data without recourse to a discourse parser or additional annotations.
Drawing inspiration from recent efforts to empower neural networks with a
structural bias, we propose a model that can encode a document while
automatically inducing rich structural dependencies. Specifically, we embed a
differentiable non-projective parsing algorithm into a neural model and use
attention mechanisms to incorporate the structural biases. Experimental
evaluation across different tasks and datasets shows that the proposed model
achieves state-of-the-art results on document modeling tasks while inducing
intermediate structures which are both interpretable and meaningful.Comment: change to one-based indexing, published in Transactions of the
Association for Computational Linguistics (TACL),
https://transacl.org/ojs/index.php/tacl/article/view/1185/28
TDAM: a topic-dependent attention model for sentiment analysis
We propose a topic-dependent attention model for sentiment classification and topic extraction. Our model assumes that a global topic embedding is shared across documents and employs an attention mechanism to derive local topic embedding for words and sentences. These are subsequently incorporated in a modified Gated Recurrent Unit (GRU) for sentiment classification and extraction of topics bearing different sentiment polarities. Those topics emerge from the words' local topic embeddings learned by the internal attention of the GRU cells in the context of a multi-task learning framework. In this paper, we present the hierarchical architecture, the new GRU unit and the experiments conducted on users' reviews which demonstrate classification performance on a par with the state-of-the-art methodologies for sentiment classification and topic coherence outperforming the current approaches for supervised topic extraction. In addition, our model is able to extract coherent aspect-sentiment clusters despite using no aspect-level annotations for training
Document-Level Relation Extraction with Adaptive Thresholding and Localized Context Pooling
Document-level relation extraction (RE) poses new challenges compared to its
sentence-level counterpart. One document commonly contains multiple entity
pairs, and one entity pair occurs multiple times in the document associated
with multiple possible relations. In this paper, we propose two novel
techniques, adaptive thresholding and localized context pooling, to solve the
multi-label and multi-entity problems. The adaptive thresholding replaces the
global threshold for multi-label classification in the prior work with a
learnable entities-dependent threshold. The localized context pooling directly
transfers attention from pre-trained language models to locate relevant context
that is useful to decide the relation. We experiment on three document-level RE
benchmark datasets: DocRED, a recently released large-scale RE dataset, and two
datasets CDRand GDA in the biomedical domain. Our ATLOP (Adaptive Thresholding
and Localized cOntext Pooling) model achieves an F1 score of 63.4, and also
significantly outperforms existing models on both CDR and GDA.Comment: Accepted by AAAI 2021. Code available at
https://github.com/wzhouad/ATLO