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Found 18 papers, 2 papers with code
ITNLP2022 at SemEval-2022 Task 8: Pre-trained Model with Data Augmentation and Voting for Multilingual News Similarity
This article introduces a system to solve the SemEval 2022 Task 8: Multilingual News Article Similarity.
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations
However, these models neglect direct utterance-knowledge interaction.
Ranked #14 on
Emotion Recognition in Conversation
on DailyDialog
Faithful Knowledge Graph Explanations for Commonsense Reasoning
While fusing language models and knowledge graphs has become common in commonsense question answering research, enabling faithful chain-of-thought explanations in these models remains an open problem.
Pre-training Language Models with Deterministic Factual Knowledge
Further experiments on question-answering datasets show that trying to learn a deterministic relationship with the proposed methods can also help other knowledge-intensive tasks.
How Pre-trained Language Models Capture Factual Knowledge? A Causal-Inspired Analysis
We check the words that have three typical associations with the missing words: knowledge-dependent, positionally close, and highly co-occurred.
Integrating Regular Expressions with Neural Networks via DFA
Human-designed rules are widely used to build industry applications.
ITNLP at SemEval-2021 Task 11: Boosting BERT with Sampling and Adversarial Training for Knowledge Extraction
This paper describes the winning system in the End-to-end Pipeline phase for the NLPContributionGraph task.
HopRetriever: Retrieve Hops over Wikipedia to Answer Complex Questions
In this paper, we propose a new retrieval target, hop, to collect the hidden reasoning evidence from Wikipedia for complex question answering.
Ranked #6 on
Question Answering
on HotpotQA
LocalGAN: Modeling Local Distributions for Adversarial Response Generation
This paper presents a new methodology for modeling the local semantic distribution of responses to a given query in the human-conversation corpus, and on this basis, explores a specified adversarial learning mechanism for training Neural Response Generation (NRG) models to build conversational agents.
ITNLP-ARC at SemEval-2018 Task 12: Argument Reasoning Comprehension with Attention
Semantic Evaluation (SemEval) 2018 Task 12 {``}The Argument Reasoning Comprehension{''} committed to research natural language reasoning.
Neural Response Generation via GAN with an Approximate Embedding Layer
This paper presents a Generative Adversarial Network (GAN) to model single-turn short-text conversations, which trains a sequence-to-sequence (Seq2Seq) network for response generation simultaneously with a discriminative classifier that measures the differences between human-produced responses and machine-generated ones.
ITNLP-AiKF at SemEval-2017 Task 1: Rich Features Based SVR for Semantic Textual Similarity Computing
Semantic Textual Similarity (STS) devotes to measuring the degree of equivalence in the underlying semantic of the sentence pair.
Learning Natural Language Inference using Bidirectional LSTM model and Inner-Attention
In our approach, the encoding of sentence is a two-stage process.
Ranked #73 on
Natural Language Inference
on SNLI
Incorporating Loose-Structured Knowledge into Conversation Modeling via Recall-Gate LSTM
Modeling human conversations is the essence for building satisfying chat-bots with multi-turn dialog ability.
