Search Results for author:
Found 11 papers, 6 papers with code
ConRPG: Paraphrase Generation using Contexts as Regularizer
A long-standing issue with paraphrase generation is how to obtain reliable supervision signals.
BertGCN: Transductive Text Classification by Combining GCN and BERT
In this work, we propose BertGCN, a model that combines large scale pretraining and transductive learning for text classification.
Ranked #1 on
Text Classification
on 20 Newsgroups
OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Visual Contexts
Based on this dataset, we propose a family of encoder-decoder models leveraging both textual and visual contexts, from coarse-grained image features extracted from CNNs to fine-grained object features extracted from Faster R-CNNs.
Self-Explaining Structures Improve NLP Models
The proposed model comes with the following merits: (1) span weights make the model self-explainable and do not require an additional probing model for interpretation; (2) the proposed model is general and can be adapted to any existing deep learning structures in NLP; (3) the weight associated with each text span provides direct importance scores for higher-level text units such as phrases and sentences.
Ranked #2 on
Sentiment Analysis
on SST-5 Fine-grained classification
(using extra training data)
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection
In this way, the model is able to select the most salient nodes and reduce the quadratic complexity regardless of the sequence length.
Non-Autoregressive Neural Dialogue Generation
Unfortunately, under the framework of the \sts model, direct decoding from $\log p(y|x) + \log p(x|y)$ is infeasible since the second part (i. e., $p(x|y)$) requires the completion of target generation before it can be computed, and the search space for $y$ is enormous.
Description Based Text Classification with Reinforcement Learning
We observe significant performance boosts over strong baselines on a wide range of text classification tasks including single-label classification, multi-label classification and multi-aspect sentiment analysis.
A Unified MRC Framework for Named Entity Recognition
Instead of treating the task of NER as a sequence labeling problem, we propose to formulate it as a machine reading comprehension (MRC) task.
Ranked #2 on
Nested Mention Recognition
on ACE 2004
(using extra training data)
Chinese Named Entity Recognition
Entity Extraction using GAN
+4
Is Word Segmentation Necessary for Deep Learning of Chinese Representations?
Based on these observations, we conduct comprehensive experiments to study why word-based models underperform char-based models in these deep learning-based NLP tasks.
Glyce: Glyph-vectors for Chinese Character Representations
However, due to the lack of rich pictographic evidence in glyphs and the weak generalization ability of standard computer vision models on character data, an effective way to utilize the glyph information remains to be found.
Ranked #1 on
Chinese Word Segmentation
on AS
Chinese Dependency Parsing
Chinese Named Entity Recognition
+19
