Search Results for author:
Found 32 papers, 16 papers with code
RST Discourse Parsing with Second-Stage EDU-Level Pre-training
Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i. e. element discourse unit (EDU). To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models. Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP). We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation. Experimental results on a benckmark dataset show that our method is highly effective, leading a 2. 1-point improvement in F1-score. All codes and pre-trained models will be released publicly to facilitate future studies.
Tracking Satisfaction States for Customer Satisfaction Prediction in E-commerce Service Chatbots
In this work, we investigate the problem of satisfaction states tracking and its effects on CSP in E-commerce service chatbots.
A Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation
In this paper, we investigate the importance of discourse structures in handling informative contextual cues and speaker-specific features for ERMC.
Ranked #20 on
Emotion Recognition in Conversation
on EmoryNLP
Speaker-Aware Discourse Parsing on Multi-Party Dialogues
It is believed that speaker interactions are helpful for this task.
Ranked #2 on
Discourse Parsing
on STAC
Chinese Opinion Role Labeling with Corpus Translation: A Pivot Study
Opinion Role Labeling (ORL), aiming to identify the key roles of opinion, has received increasing interest.
Non-autoregressive Text Editing with Copy-aware Latent Alignments
In this work, we propose a novel non-autoregressive text editing method to circumvent the above issues, by modeling the edit process with latent CTC alignments.
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
RSpell: Retrieval-augmented Framework for Domain Adaptive Chinese Spelling Check
In this paper, we propose a retrieval-augmented spelling check framework called RSpell, which searches corresponding domain terms and incorporates them into CSC models.
Discourse-Aware Emotion Cause Extraction in Conversations
In this paper, we investigate the importance of discourse structures in handling utterance interactions and conversationspecific features for ECEC.
Ranked #5 on
Causal Emotion Entailment
on RECCON
Visual Subtitle Feature Enhanced Video Outline Generation
Furthermore, based on the similarity between video outlines and textual outlines, we use a large number of articles with chapter headings to pretrain our model.
Revising Image-Text Retrieval via Multi-Modal Entailment
While the builders of existing image-text retrieval datasets strive to ensure that the caption matches the linked image, they cannot prevent a caption from fitting other images.
General and Domain Adaptive Chinese Spelling Check with Error Consistent Pretraining
However, there is a big gap between the real input scenario and automatic generated corpus.
Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures Inside Arguments
Semantic role labeling (SRL) is a fundamental yet challenging task in the NLP community.
Dependency Parsing
Semantic Role Labeling (predicted predicates)
Sentence Matching with Syntax- and Semantics-Aware BERT
Sentence matching aims to identify the special relationship between two sentences, and plays a key role in many natural language processing tasks.
Unseen Target Stance Detection with Adversarial Domain Generalization
Although stance detection has made great progress in the past few years, it is still facing the problem of unseen targets.
Cue-word Driven Neural Response Generation with a Shrinking Vocabulary
Open-domain response generation is the task of generating sensible and informative re-sponses to the source sentence.
Syntax-aware Neural Semantic Role Labeling
Semantic role labeling (SRL), also known as shallow semantic parsing, is an important yet challenging task in NLP.
Enhancing Opinion Role Labeling with Semantic-Aware Word Representations from Semantic Role Labeling
Opinion role labeling (ORL) is an important task for fine-grained opinion mining, which identifies important opinion arguments such as holder and target for a given opinion trigger.
Ranked #1 on
Fine-Grained Opinion Analysis
on MPQA
(using extra training data)
Syntax-Enhanced Neural Machine Translation with Syntax-Aware Word Representations
Syntax has been demonstrated highly effective in neural machine translation (NMT).
Ranked #8 on
Machine Translation
on IWSLT2015 English-Vietnamese
Effective Subword Segmentation for Text Comprehension
Representation learning is the foundation of machine reading comprehension and inference.
Transition-based Neural RST Parsing with Implicit Syntax Features
Syntax has been a useful source of information for statistical RST discourse parsing.
Ranked #7 on
Discourse Parsing
on RST-DT
End-to-End Neural Relation Extraction with Global Optimization
Neural networks have shown promising results for relation extraction.
Ranked #1 on
Relation Extraction
on ACE 2005
(Sentence Encoder metric)
Recognizing irregular entities in biomedical text via deep neural networks
We evaluate our models on two datasets for recognizing regular and irreg- ular biomedical entities.
Joint POS Tagging and Dependency Parsing with Transition-based Neural Networks
While part-of-speech (POS) tagging and dependency parsing are observed to be closely related, existing work on joint modeling with manually crafted feature templates suffers from the feature sparsity and incompleteness problems.
Tweet Sarcasm Detection Using Deep Neural Network
We investigate the use of neural network for tweet sarcasm detection, and compare the effects of the continuous automatic features with discrete manual features.
A Bi-LSTM-RNN Model for Relation Classification Using Low-Cost Sequence Features
This model divides a sentence or text segment into five parts, namely two target entities and their three contexts.
