14 research outputs found
Lexico-syntactic Text Simplification And Compression With Typed Dependencies
We describe two systems for text simplification using typed dependency structures, one that performs lexical and syntactic simplification, and another that performs sentence compression optimised to satisfy global text constraints such as lexical density, the ratio of difficult words, and text length. We report a substantial evaluation that demonstrates the superiority of our systems, individually and in combination, over the state of the art, and also report a comprehension based evaluation of contemporary automatic text simplification systems with target non-native readers
Do not let the history haunt you - Mitigating Compounding Errors in Conversational Question Answering
The Conversational Question Answering (CoQA) task involves answering a
sequence of inter-related conversational questions about a contextual
paragraph. Although existing approaches employ human-written ground-truth
answers for answering conversational questions at test time, in a realistic
scenario, the CoQA model will not have any access to ground-truth answers for
the previous questions, compelling the model to rely upon its own previously
predicted answers for answering the subsequent questions. In this paper, we
find that compounding errors occur when using previously predicted answers at
test time, significantly lowering the performance of CoQA systems. To solve
this problem, we propose a sampling strategy that dynamically selects between
target answers and model predictions during training, thereby closely
simulating the situation at test time. Further, we analyse the severity of this
phenomena as a function of the question type, conversation length and domain
type
Contextualised Graph Attention for Improved Relation Extraction
This paper presents a contextualized graph attention network that combines edge features and multiple sub-graphs for improving relation extraction. A novel method is proposed to use multiple sub-graphs to learn rich node representations in graph-based networks. To this end multiple sub-graphs are obtained from a single dependency tree. Two types of edge features are proposed, which are effectively combined with GAT and GCN models to apply for relation extraction. The proposed model achieves state-of-the-art performance on Semeval 2010 Task 8 dataset, achieving an F1-score of 86.3
Combining Long Short Term Memory and Convolutional Neural Network for Cross-Sentence n-ary Relation Extraction
We propose in this paper a combined model of Long Short Term Memory and Convolutional Neural Networks (LSTM-CNN) that exploits word embeddings and positional embeddings for cross-sentence n-ary relation extraction. The proposed model brings together the properties of both LSTMs and CNNs, to simultaneously exploit long-range sequential information and capture most informative features, essential for cross-sentence n-ary relation extraction. The LSTM-CNN model is evaluated on standard dataset on cross-sentence n-ary relation extraction, where it significantly outperforms baselines such as CNNs, LSTMs and also a combined CNN-LSTM model. The paper also shows that the LSTM-CNN model outperforms the current state-of-the-art methods on cross-sentence n-ary relation extraction