Search Results for author:
Found 74 papers, 22 papers with code
A Dependency-Based Neural Network for Relation Classification
Previous research on relation classification has verified the effectiveness of using dependency shortest paths or subtrees.
Ranked #5 on
Relation Classification
on SemEval 2010 Task 8
Towards Label Imbalance in Multi-label Classification with Many Labels
To the best of our knowledge, we are the first to tackle the imbalance problem in multi-label classification with many labels.
Bi-LSTM Neural Networks for Chinese Grammatical Error Diagnosis
Grammatical Error Diagnosis for Chinese has always been a challenge for both foreign learners and NLP researchers, for the variousity of grammar and the flexibility of expression.
Improving Semantic Relevance for Sequence-to-Sequence Learning of Chinese Social Media Text Summarization
In this work, our goal is to improve semantic relevance between source texts and summaries for Chinese social media summarization.
meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting
In back propagation, only a small subset of the full gradient is computed to update the model parameters.
A Two-Stage Parsing Method for Text-Level Discourse Analysis
Previous work introduced transition-based algorithms to form a unified architecture of parsing rhetorical structures (including span, nuclearity and relation), but did not achieve satisfactory performance.
Ranked #5 on
Discourse Parsing
on RST-DT
Noise-Clustered Distant Supervision for Relation Extraction: A Nonparametric Bayesian Perspective
For the task of relation extraction, distant supervision is an efficient approach to generate labeled data by aligning knowledge base with free texts.
Learning to Rank Semantic Coherence for Topic Segmentation
Topic segmentation plays an important role for discourse parsing and information retrieval.
Interactive Attention Networks for Aspect-Level Sentiment Classification
In this paper, we argue that both targets and contexts deserve special treatment and need to be learned their own representations via interactive learning.
Addressing Domain Adaptation for Chinese Word Segmentation with Global Recurrent Structure
Boundary features are widely used in traditional Chinese Word Segmentation (CWS) methods as they can utilize unlabeled data to help improve the Out-of-Vocabulary (OOV) word recognition performance.
Cascading Multiway Attentions for Document-level Sentiment Classification
Document-level sentiment classification aims to assign the user reviews a sentiment polarity.
Ranked #5 on
Sentiment Analysis
on User and product information
Training Simplification and Model Simplification for Deep Learning: A Minimal Effort Back Propagation Method
Based on the sparsified gradients, we further simplify the model by eliminating the rows or columns that are seldom updated, which will reduce the computational cost both in the training and decoding, and potentially accelerate decoding in real-world applications.
Complex Structure Leads to Overfitting: A Structure Regularization Decoding Method for Natural Language Processing
The decoding of the complex structure model is regularized by the additionally trained simple structure model.
Tag-Enhanced Tree-Structured Neural Networks for Implicit Discourse Relation Classification
Identifying implicit discourse relations between text spans is a challenging task because it requires understanding the meaning of the text.
General Classification
Implicit Discourse Relation Classification
+3
Autoencoder as Assistant Supervisor: Improving Text Representation for Chinese Social Media Text Summarization
In this work, we supervise the learning of the representation of the source content with that of the summary.
Unpaired Sentiment-to-Sentiment Translation: A Cycled Reinforcement Learning Approach
We evaluate our approach on two review datasets, Yelp and Amazon.
Ranked #6 on
Unsupervised Text Style Transfer
on Yelp
SGM: Sequence Generation Model for Multi-label Classification
Further analysis of experimental results demonstrates that the proposed methods not only capture the correlations between labels, but also select the most informative words automatically when predicting different labels.
Question Condensing Networks for Answer Selection in Community Question Answering
Answer selection is an important subtask of community question answering (CQA).
A Neural Question Answering Model Based on Semi-Structured Tables
Then the system measures the relevance between each question and candidate table cells, and choose the most related cell as the source of answer.
Sememe Prediction: Learning Semantic Knowledge from Unstructured Textual Wiki Descriptions
In the experiments, we take a real-world sememe knowledge base HowNet and the corresponding descriptions of the words in Baidu Wiki for training and evaluation.
simNet: Stepwise Image-Topic Merging Network for Generating Detailed and Comprehensive Image Captions
The encode-decoder framework has shown recent success in image captioning.
Phrase-level Self-Attention Networks for Universal Sentence Encoding
As a result, the memory consumption can be reduced because the self-attention is performed at the phrase level instead of the sentence level.
Joint Learning for Targeted Sentiment Analysis
Targeted sentiment analysis (TSA) aims at extracting targets and classifying their sentiment classes.
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning
The lack of labeled data is one of the main challenges when building a task-oriented dialogue system.
Exploring Sequence-to-Sequence Learning in Aspect Term Extraction
Aspect term extraction (ATE) aims at identifying all aspect terms in a sentence and is usually modeled as a sequence labeling problem.
Ranked #1 on
Term Extraction
on SemEval 2014 Task 4 Laptop
Text Level Graph Neural Network for Text Classification
Recently, researches have explored the graph neural network (GNN) techniques on text classification, since GNN does well in handling complex structures and preserving global information.
Ranked #2 on
Text Classification
on Ohsumed
Syntax-Aware Graph Attention Network for Aspect-Level Sentiment Classification
In this paper, we exploit syntactic awareness to the model by the graph attention network on the dependency tree structure and external pre-training knowledge by BERT language model, which helps to model the interaction between the context and aspect words better.
Improving Machine Translation by Searching Skip Connections Efficiently
To improve the efficiency of trying different skip connection architectures, we apply the idea of network morphism to add skip connections as a procedure of fine-tuning.
First Target and Opinion then Polarity: Enhancing Target-opinion Correlation for Aspect Sentiment Triplet Extraction
Aspect Sentiment Triplet Extraction (ASTE) aims to extract triplets from a sentence, including target entities, associated sentiment polarities, and opinion spans which rationalize the polarities.
Ranked #8 on
Aspect Sentiment Triplet Extraction
on ASTE-Data-V2
Instantaneous Grammatical Error Correction with Shallow Aggressive Decoding
In this paper, we propose Shallow Aggressive Decoding (SAD) to improve the online inference efficiency of the Transformer for instantaneous Grammatical Error Correction (GEC).
A Joint and Domain-Adaptive Approach to Spoken Language Understanding
In this paper, we attempt to bridge these two lines of research and propose a joint and domain adaptive approach to SLU.
Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking
Recently, researchers have explored using the encoder-decoder framework to tackle dialogue state tracking (DST), which is a key component of task-oriented dialogue systems.
Towards Controlled and Diverse Generation of Article Comments
In this paper, we make the first step towards controllable generation of comments, by building a system that can explicitly control the emotion of the generated comments.
Density-Based Dynamic Curriculum Learning for Intent Detection
To handle this problem, we propose a density-based dynamic curriculum learning model.
A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model
Synthetic data construction of Grammatical Error Correction (GEC) for non-English languages relies heavily on human-designed and language-specific rules, which produce limited error-corrected patterns.
Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt
To collocate with the unified prompt, we propose a new initialization method for the target label word to further improve the model's transferability across languages.
Using calibrator to improve robustness in Machine Reading Comprehension
Machine Reading Comprehension(MRC) has achieved a remarkable result since some powerful models, such as BERT, are proposed.
Incorporating Hierarchy into Text Encoder: a Contrastive Learning Approach for Hierarchical Text Classification
Hierarchical text classification is a challenging subtask of multi-label classification due to its complex label hierarchy.
Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss
Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.
HPT: Hierarchy-aware Prompt Tuning for Hierarchical Text Classification
However, in this paradigm, there exists a huge gap between the classification tasks with sophisticated label hierarchy and the masked language model (MLM) pretraining tasks of PLMs and thus the potentials of PLMs can not be fully tapped.
A Unified Framework for Multi-intent Spoken Language Understanding with prompting
Multi-intent Spoken Language Understanding has great potential for widespread implementation.
Semiparametric Language Models Are Scalable Continual Learners
We demonstrate that SeMem improves the scalability of semiparametric LMs for continual learning over streaming data in two ways: (1) data-wise scalability: as the model becomes stronger through continual learning, it will encounter fewer difficult cases that need to be memorized, causing the growth of the non-parametric memory to slow down over time rather than growing at a linear rate with the size of training data; (2) model-wise scalability: SeMem allows a larger model to memorize fewer samples than its smaller counterpart because it is rarer for a larger model to encounter incomprehensible cases, resulting in a non-parametric memory that does not scale linearly with model size.
Preference Ranking Optimization for Human Alignment
In this manner, PRO effectively transforms human alignment into aligning the probability ranking of n responses generated by LLM with the preference ranking of humans towards these responses.
ICDPO: Effectively Borrowing Alignment Capability of Others via In-context Direct Preference Optimization
Large Language Models (LLMs) rely on Human Preference Alignment (HPA) to ensure the generation of safe content.
Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification
Hierarchical text classification (HTC) is a challenging subtask of multi-label classification due to its complex taxonomic structure.
Scaling Data Diversity for Fine-Tuning Language Models in Human Alignment
Additionally, the concept of diversity for prompts can be more complex than responses that are typically quantified by single digits.
Multi-Layer Pseudo-Siamese Biaffine Model for Dependency Parsing
However, previous work only used the biaffine method at the end of the dependency parser as a scorer, and its application in multi-layer form is ignored.
Adjusting the Precision-Recall Trade-Off with Align-and-Predict Decoding for Grammatical Error Correction
Modern writing assistance applications are always equipped with a Grammatical Error Correction (GEC) model to correct errors in user-entered sentences.
Original Content Is All You Need! an Empirical Study on Leveraging Answer Summary for WikiHowQA Answer Selection Task
And their experiments show that leveraging the answer summaries helps to attend the essential information in original lengthy answers and improve the answer selection performance under certain circumstances.
