Search Results for author:
Found 12 papers, 1 papers with code
Document-Grounded Goal-Oriented Dialogue Systems on Pre-Trained Language Model with Diverse Input Representation
In this paper, we propose two models (i. e., a knowledge span prediction model and a response generation model) for the subtask1 and the subtask2.
The Pipeline Model for Resolution of Anaphoric Reference and Resolution of Entity Reference
In this paper, we propose the pipelined model (i. e., a resolution of anaphoric identity and a resolution of bridging references) for the subtask1 and the subtask2.
Improving Sentence-Level Relation Extraction through Curriculum Learning
Sentence-level relation extraction mainly aims to classify the relation between two entities in a sentence.
Ranked #1 on
Relation Extraction
on Re-TACRED
Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis
In this paper, we propose Deep Contextualized Relation-Aware Network (DCRAN), which allows interactive relations among subtasks with deep contextual information based on two modules (i. e., Aspect and Opinion Propagation and Explicit Self-Supervised Strategies).
Korean-English Machine Translation with Multiple Tokenization Strategy
This work was conducted to find out how tokenization methods affect the training results of machine translation models.
Grammar Accuracy Evaluation (GAE): Quantifiable Intrinsic Evaluation of Machine Translation Models
Intrinsic evaluation by humans for the performance of natural language generation models is conducted to overcome the fact that the quality of generated sentences cannot be fully represented by only extrinsic evaluation.
Improving Document-Level Sentiment Classification Using Importance of Sentences
Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences.
Ranked #1 on
Document Classification
on IMDb-M
Dual Pointer Network for Fast Extraction of Multiple Relations in a Sentence
The proposed model finds n-to-1 subject-object relations using a forward object decoder.
Ranked #1 on
Relation Extraction
on ACE 2005
(Relation classification F1 metric)
Relation Extraction among Multiple Entities Using a Dual Pointer Network with a Multi-Head Attention Mechanism
The proposed model finds n-to-1 subject-object relations by using a forward de-coder called an object decoder.
Ranked #1 on
Relation Extraction
on ACE 2005
(Cross Sentence metric)
ThisIsCompetition at SemEval-2019 Task 9: BERT is unstable for out-of-domain samples
This paper describes our system, Joint Encoders for Stable Suggestion Inference (JESSI), for the SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.
