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Works184 from public data
- CodeBERT: A Pre-Trained Model for Programming and Natural Languages4,421
This work develops CodeBERT with Transformer-based neural architecture, and trains it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators.
- Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification1,229
Three neural networks are developed to effectively incorporate the supervision from sentiment polarity of text (e.g. sentences or tweets) in their loss functions and the performance of SSWE is improved by concatenating SSWE with existing feature set.
- Aspect Level Sentiment Classification with Deep Memory Network1,041
A deep memory network for aspect level sentiment classification that explicitly captures the importance of each context word when inferring the sentiment polarity of an aspect, and is also fast.
- Effective LSTMs for Target-Dependent Sentiment Classification1,010
Two target dependent long short-term memory models, where target information is automatically taken into account, are developed, which achieve state-of-the-art performances without using syntactic parser or external sentiment lexicons.
- Learning Semantic Representations of Users and Products for Document Level Sentiment Classification365
By combining evidence at user-, product and documentlevel in a unified neural framework, the proposed model achieves state-of-the-art performances on IMDB and Yelp datasets1.
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- Recall and Learn: Fine-tuning Deep Pretrained Language Models with Less Forgetting278
A recall and learn mechanism, which adopts the idea of multi-task learning and jointly learns pretraining tasks and downstream tasks, and an Objective Shifting mechanism to focus the learning on downstream tasks gradually.
- Coooolll: A Deep Learning System for Twitter Sentiment Classification251
A neural network with hybrid loss function 1 is developed to learn SSWE, which encodes the sentiment information of tweets in the continuous representation of words, which can be easily re-implemented with the publicly available sentiment-specific word embedding.
- Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network210
A Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the state-of-the-art few-shot classification model – TapNet is proposed, by leveraging label name semantics in representing labels.
- Deep learning for sentiment analysis: successful approaches and future challenges207
An overview of the successful deep learning approaches for sentiment analysis tasks is provided, lay out the remaining challenges and provide some suggestions to address these challenges.
- N-LTP: An Open-source Neural Language Technology Platform for Chinese172
N-LTP, an open-source neural language technology platform supporting six fundamental Chinese NLP tasks: lexical analysis (Chinese word segmentation, part-of-speech tagging, and named entity recognition), syntactic parsing (dependency parsing), and semantic parsing (semantic dependency parsing and semantic role labeling).
- A Co-Interactive Transformer for Joint Slot Filling and Intent Detection156
This paper proposes a co-interactive module to consider the cross-impact by building a bidirectional connection between the two related tasks, where slot and intent can be able to attend on the corresponding mutual information.
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- CharBERT: Character-aware Pre-trained Language Model131
This paper proposes a character-aware pre-trained language model named CharBERT improving on the previous methods (such as BERT, RoBERTa) and proposes a new pre-training task named NLM (Noisy LM) for unsupervised character representation learning.
- Revisiting Embedding Features for Simple Semi-supervised Learning127
Experiments on the task of named entity recognition show that each of the proposed approaches can better utilize the word embedding features, among which the distributional prototype approach performs the best.
- Molweni: A Challenge Multiparty Dialogues-based Machine Reading Comprehension Dataset with Discourse Structure125
The Molweni dataset is presented, a machine reading comprehension (MRC) dataset with discourse structure built over multiparty dialog with large-scale annotations in a modified Segmented Discourse Representation Theory (SDRT; Asher et al., 2016) style.
- A Neural Multi-Task Learning Framework to Jointly Model Medical Named Entity Recognition and Normalization123
A novel deep neural multi-task learning framework with explicit feedback strategies to jointly model recognition and normalization that performs significantly better than state-of-the-art approaches on two publicly available medical literature datasets.
- Language Model as an Annotator: Exploring DialoGPT for Dialogue Summarization114
DialoGPT, a pre-trained model for conversational response generation, can be developed as an unsupervised dialogue annotator, which takes advantage of dialogue background knowledge encoded in DialoG PT.
- Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation101
- Event causality extraction based on connectives analysis90
The advantage of this model lies in its ability to cope with partial interactions among features so as to avoid over-fitting problem on Hidden Naive Bayes model, especially the interaction between the connective category and the syntactic structure of sentences.
- Pivot Approach for Extracting Paraphrase Patterns from Bilingual Corpora86
A loglinear model is proposed to compute the paraphrase likelihood of two patterns and exploit feature functions based on maximum likelihood estimation (MLE) and lexical weighting (LW) to extract paraphrase patterns from bilingual parallel corpora.
- An Entity-Mention Model for Coreference Resolution with Inductive Logic Programming86
An expressive entity-mention model that performs coreference resolution at an entity level that adopts the Inductive Logic Programming (ILP) algorithm, which provides a relational way to organize different knowledge of entities and mentions.
- Semantic Parsing with Syntax- and Table-Aware SQL Generation83
The quality of the generated SQL query is significantly improved through learning to replicate content from column names, cells or SQL keywords; and improving the generation of WHERE clause by leveraging the column-cell relation.
- Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources79
This paper addresses the problem of representing words with multiple and sense-specific embeddings, which are learned from bilingual parallel data by representing each word with a single embedding, which fails to capture polysemy.
- Hierarchical Attention Flow for Multiple-Choice Reading Comprehension78
This paper presents the hierarchical attention flow to adequately leverage candidate options to model the interactions among passages, questions and candidate options and explicitly model the option correlations with attention mechanism to obtain better option representations.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-13. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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