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Found 44 papers, 22 papers with code
Constructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation
To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotional consensus and utilize some external unpaired data.
MedDiT: A Knowledge-Controlled Diffusion Transformer Framework for Dynamic Medical Image Generation in Virtual Simulated Patient
Additionally, a well-tuned Diffusion Transformer (DiT) model is incorporated to generate medical images according to the specified patient attributes in the KG.
Mobius: A High Efficient Spatial-Temporal Parallel Training Paradigm for Text-to-Video Generation Task
However, the traditional 3D-Unet is a serial mode and the temporal layers follow the spatial layers, which will result in high GPU memory and training time consumption according to its serial feature flow.
Rephrasing the Reference for Non-Autoregressive Machine Translation
In response to this problem, we introduce a rephraser to provide a better training target for NAT by rephrasing the reference sentence according to the NAT output.
AutoCAD: Automatically Generating Counterfactuals for Mitigating Shortcut Learning
Recent studies have shown the impressive efficacy of counterfactually augmented data (CAD) for reducing NLU models' reliance on spurious features and improving their generalizability.
Counterfactual Data Augmentation via Perspective Transition for Open-Domain Dialogues
The dialogue data admits a wide variety of responses for a given dialogue history, especially responses with different semantics.
Selecting Stickers in Open-Domain Dialogue through Multitask Learning
With the increasing popularity of online chatting, stickers are becoming important in our online communication.
Mental Health Assessment for the Chatbots
Previous researches on dialogue system assessment usually focus on the quality evaluation (e. g. fluency, relevance, etc) of responses generated by the chatbots, which are local and technical metrics.
Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation
To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotion consensus and utilize some external unpaired data.
Improving Gradient-based Adversarial Training for Text Classification by Contrastive Learning and Auto-Encoder
(2) RAR forces the model to reconstruct the original sample from its adversarial representation.
Challenging Instances are Worth Learning: Generating Valuable Negative Samples for Response Selection Training
In this way, our negative instances are fluent, context-related, and more challenging for the model to learn, while can not be positive.
Different Strokes for Different Folks: Investigating Appropriate Further Pre-training Approaches for Diverse Dialogue Tasks
Loading models pre-trained on the large-scale corpus in the general domain and fine-tuning them on specific downstream tasks is gradually becoming a paradigm in Natural Language Processing.
Structure-Enhanced Pop Music Generation via Harmony-Aware Learning
Experimental results reveal that compared to the existing methods, HAT owns a much better understanding of the structure and it can also improve the quality of generated music, especially in the form and texture.
Guiding Topic Flows in the Generative Chatbot by Enhancing the ConceptNet with the Conversation Corpora
Further analysis individually proves the effectiveness of the enhanced concept graph and the Edge-Transformer architecture.
Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark
Compared to previous dialogue tasks, MOD is much more challenging since it requires the model to understand the multimodal elements as well as the emotions behind them.
Text AutoAugment: Learning Compositional Augmentation Policy for Text Classification
Data augmentation aims to enrich training samples for alleviating the overfitting issue in low-resource or class-imbalanced situations.
Sequence-Level Training for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) removes the autoregressive mechanism and achieves significant decoding speedup through generating target words independently and simultaneously.
GTM: A Generative Triple-Wise Model for Conversational Question Generation
Generating some appealing questions in open-domain conversations is an effective way to improve human-machine interactions and lead the topic to a broader or deeper direction.
Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances
Nowadays, open-domain dialogue models can generate acceptable responses according to the historical context based on the large-scale pre-trained language models.
Addressing Inquiries about History: An Efficient and Practical Framework for Evaluating Open-domain Chatbot Consistency
Employing human judges to interact with chatbots on purpose to check their capacities is costly and low-efficient, and difficult to get rid of subjective bias.
WeChat AI & ICT's Submission for DSTC9 Interactive Dialogue Evaluation Track
We participate in the DSTC9 Interactive Dialogue Evaluation Track (Gunasekara et al. 2020) sub-task 1 (Knowledge Grounded Dialogue) and sub-task 2 (Interactive Dialogue).
One Comment from One Perspective: An Effective Strategy for Enhancing Automatic Music Comment
The automatic generation of music comments is of great significance for increasing the popularity of music and the music platform{'}s activity.
Contrastive Zero-Shot Learning for Cross-Domain Slot Filling with Adversarial Attack
In this paper, we propose a Contrastive Zero-Shot Learning with Adversarial Attack (CZSL-Adv) method for the cross-domain slot filling.
Token-level Adaptive Training for Neural Machine Translation
The vanilla NMT model usually adopts trivial equal-weighted objectives for target tokens with different frequencies and tends to generate more high-frequency tokens and less low-frequency tokens compared with the golden token distribution.
Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis
For multiple aspects scenario of aspect-based sentiment analysis (ABSA), existing approaches typically ignore inter-aspect relations or rely on temporal dependencies to process aspect-aware representations of all aspects in a sentence.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
Recent studies in dialogue state tracking (DST) leverage historical information to determine states which are generally represented as slot-value pairs.
Ranked #6 on
Multi-domain Dialogue State Tracking
on MULTIWOZ 2.1
Dialogue State Tracking
Multi-domain Dialogue State Tracking
Towards Multimodal Response Generation with Exemplar Augmentation and Curriculum Optimization
Furthermore, to facilitate the convergence of Gaussian mixture prior and posterior distributions, we devise a curriculum optimization strategy to progressively train the model under multiple training criteria from easy to hard.
A Dependency Syntactic Knowledge Augmented Interactive Architecture for End-to-End Aspect-based Sentiment Analysis
The aspect-based sentiment analysis (ABSA) task remains to be a long-standing challenge, which aims to extract the aspect term and then identify its sentiment orientation. In previous approaches, the explicit syntactic structure of a sentence, which reflects the syntax properties of natural language and hence is intuitively crucial for aspect term extraction and sentiment recognition, is typically neglected or insufficiently modeled.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
An Iterative Multi-Knowledge Transfer Network for Aspect-Based Sentiment Analysis
Aspect-based sentiment analysis (ABSA) mainly involves three subtasks: aspect term extraction, opinion term extraction, and aspect-level sentiment classification, which are typically handled in a separate or joint manner.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+3
Bridging Text and Video: A Universal Multimodal Transformer for Video-Audio Scene-Aware Dialog
Audio-Visual Scene-Aware Dialog (AVSD) is a task to generate responses when chatting about a given video, which is organized as a track of the 8th Dialog System Technology Challenge (DSTC8).
Minimizing the Bag-of-Ngrams Difference for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Neural Machine Translation (NAT) achieves significant decoding speedup through generating target words independently and simultaneously.
Multi-Zone Unit for Recurrent Neural Networks
Recurrent neural networks (RNNs) have been widely used to deal with sequence learning problems.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Improving Bidirectional Decoding with Dynamic Target Semantics in Neural Machine Translation
Generally, Neural Machine Translation models generate target words in a left-to-right (L2R) manner and fail to exploit any future (right) semantics information, which usually produces an unbalanced translation.
CM-Net: A Novel Collaborative Memory Network for Spoken Language Understanding
Spoken Language Understanding (SLU) mainly involves two tasks, intent detection and slot filling, which are generally modeled jointly in existing works.
Ranked #1 on
Slot Filling
on CAIS
Enhancing Context Modeling with a Query-Guided Capsule Network for Document-level Translation
Context modeling is essential to generate coherent and consistent translation for Document-level Neural Machine Translations.
A Novel Aspect-Guided Deep Transition Model for Aspect Based Sentiment Analysis
Aspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence representation generation.
Aspect-Based Sentiment Analysis
Aspect-Based Sentiment Analysis (ABSA)
+1
Retrieving Sequential Information for Non-Autoregressive Neural Machine Translation
Non-Autoregressive Transformer (NAT) aims to accelerate the Transformer model through discarding the autoregressive mechanism and generating target words independently, which fails to exploit the target sequential information.
GCDT: A Global Context Enhanced Deep Transition Architecture for Sequence Labeling
Current state-of-the-art systems for sequence labeling are typically based on the family of Recurrent Neural Networks (RNNs).
Ranked #17 on
Named Entity Recognition (NER)
on CoNLL 2003 (English)
(using extra training data)
An end-to-end Neural Network Framework for Text Clustering
Conventional \mbox{methods} generally treat this task using separated steps, including text representation learning and clustering the representations.
DTMT: A Novel Deep Transition Architecture for Neural Machine Translation
In this paper, we further enhance the RNN-based NMT through increasing the transition depth between consecutive hidden states and build a novel Deep Transition RNN-based Architecture for Neural Machine Translation, named DTMT.
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation
This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance.
