Search Results for author:
Found 17 papers, 6 papers with code
KSAM: Infusing Multi-Source Knowledge into Dialogue Generation via Knowledge Source Aware Multi-Head Decoding
Knowledge-enhanced methods have bridged the gap between human beings and machines in generating dialogue responses.
More is Better: Enhancing Open-Domain Dialogue Generation via Multi-Source Heterogeneous Knowledge
Despite achieving remarkable performance, previous knowledge-enhanced works usually only use a single-source homogeneous knowledge base of limited knowledge coverage.
Adversarial Attack against Cross-lingual Knowledge Graph Alignment
Recent literatures have shown that knowledge graph (KG) learning models are highly vulnerable to adversarial attacks.
Section-Aware Commonsense Knowledge-Grounded Dialogue Generation with Pre-trained Language Model
However, a dialogue is always aligned to a lot of retrieved fact candidates; as a result, the linearized text is always lengthy and then significantly increases the burden of using PLMs.
STBA: Towards Evaluating the Robustness of DNNs for Query-Limited Black-box Scenario
Many attack techniques have been proposed to explore the vulnerability of DNNs and further help to improve their robustness.
Cross-lingual Entity Alignment with Adversarial Kernel Embedding and Adversarial Knowledge Translation
With the guidance of known aligned entities in the context of multiple random walks, an adversarial knowledge translation model is developed to fill and translate masked entities in pairwise random walks from two KGs.
Improving Knowledge-Aware Dialogue Response Generation by Using Human-Written Prototype Dialogues
Given a query, our approach first retrieves a set of prototype dialogues that are relevant to the query.
A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction
In this paper, we propose a multi-task neural network to perform emotion-cause pair extraction in a unified model.
Ranked #11 on
Emotion-Cause Pair Extraction
on ECPE
Diverse and Informative Dialogue Generation with Context-Specific Commonsense Knowledge Awareness
We collect and build a large-scale Chinese dataset aligned with the commonsense knowledge for dialogue generation.
Detecting Tweets Mentioning Drug Name and Adverse Drug Reaction with Hierarchical Tweet Representation and Multi-Head Self-Attention
This paper describes our system for the first and third shared tasks of the third Social Media Mining for Health Applications (SMM4H) workshop, which aims to detect the tweets mentioning drug names and adverse drug reactions.
HL-EncDec: A Hybrid-Level Encoder-Decoder for Neural Response Generation
Recent years have witnessed a surge of interest on response generation for neural conversation systems.
Neural Metaphor Detecting with CNN-LSTM Model
In addition, we compare the performance of the softmax classifier and conditional random field (CRF) for sequential labeling in this task.
THU\_NGN at SemEval-2018 Task 1: Fine-grained Tweet Sentiment Intensity Analysis with Attention CNN-LSTM
In order to address this task, we propose a system based on an attention CNN-LSTM model.
THU\_NGN at SemEval-2018 Task 10: Capturing Discriminative Attributes with MLP-CNN model
Thus, the aim of SemEval-2018 Task 10 is to predict whether a word is a discriminative attribute between two concepts.
THU\_NGN at SemEval-2018 Task 2: Residual CNN-LSTM Network with Attention for English Emoji Prediction
Thus, in SemEval-2018 Task 2 an interesting and challenging task is proposed, i. e., predicting which emojis are evoked by text-based tweets.
THU\_NGN at SemEval-2018 Task 3: Tweet Irony Detection with Densely connected LSTM and Multi-task Learning
Detecting irony is an important task to mine fine-grained information from social web messages.
THU\_NGN at IJCNLP-2017 Task 2: Dimensional Sentiment Analysis for Chinese Phrases with Deep LSTM
Since the existing valence-arousal resources of Chinese are mainly in word-level and there is a lack of phrase-level ones, the Dimensional Sentiment Analysis for Chinese Phrases (DSAP) task aims to predict the valence-arousal ratings for Chinese affective words and phrases automatically.
