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Found 21 papers, 5 papers with code
EvEval: A Comprehensive Evaluation of Event Semantics for Large Language Models
In this paper, we propose an overarching framework for event semantic processing, encompassing understanding, reasoning, and prediction, along with their fine-grained aspects.
BNS: Building Network Structures Dynamically for Continual Learning
Continual learning (CL) of a sequence of tasks is often accompanied with the catastrophic forgetting(CF) problem.
Efficient Out-of-Distribution Detection via CVAE data Generation
Recently, contrastive loss with data augmentation and pseudo class creation has been shown to produce markedly better results for out-of-distribution (OOD) detection than previous methods.
Translation vs. Dialogue: A Comparative Analysis of Sequence-to-Sequence Modeling
Understanding neural models is a major topic of interest in the deep learning community.
HRN: A Holistic Approach to One Class Learning
Existing neural network based one-class learning methods mainly use various forms of auto-encoders or GAN style adversarial training to learn a latent representation of the given one class of data.
Using the Past Knowledge to Improve Sentiment Classification
It proposes a new lifelong learning model (called L2PG) that can retain and selectively transfer the knowledge learned in the past to help learn the new task.
Text Classification with Novelty Detection
In this paper, we propose a significantly more effective approach that converts the original problem to a pair-wise matching problem and then outputs how probable two instances belong to the same class.
Feature Projection for Improved Text Classification
In this paper, we propose a novel angle to further improve this representation learning, i. e., feature projection.
Query-bag Matching with Mutual Coverage for Information-seeking Conversations in E-commerce
Information-seeking conversation system aims at satisfying the information needs of users through conversations.
Transformation of Dense and Sparse Text Representations
The key idea of the proposed approach is to use a Forward Transformation to transform dense representations to sparse representations.
Who Is Speaking to Whom? Learning to Identify Utterance Addressee in Multi-Party Conversations
Previous research on dialogue systems generally focuses on the conversation between two participants, yet multi-party conversations which involve more than two participants within one session bring up a more complicated but realistic scenario.
Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders
In this work, we improve the WAE for response generation.
Learning from Positive and Unlabeled Data with Adversarial Training
Positive-unlabeled (PU) learning learns a binary classifier using only positive and unlabeled examples without labeled negative examples.
FlexNER: A Flexible LSTM-CNN Stack Framework for Named Entity Recognition
Named entity recognition (NER) is a foundational technology for information extraction.
One Time of Interaction May Not Be Enough: Go Deep with an Interaction-over-Interaction Network for Response Selection in Dialogues
Currently, researchers have paid great attention to retrieval-based dialogues in open-domain.
Ranked #12 on
Conversational Response Selection
on E-commerce
GSN: A Graph-Structured Network for Multi-Party Dialogues
Existing neural models for dialogue response generation assume that utterances are sequentially organized.
A Guider Network for Multi-Dual Learning
A large amount of parallel data is needed to train a strong neural machine translation (NMT) system.
Overcoming Catastrophic Forgetting via Model Adaptation
Several continual learning methods have been proposed to address the problem.
Topic-Based Question Generation
In the paper, we propose a new question generation problem, which also requires the input of a target topic in addition to a piece of descriptive text.
Neural System Combination for Machine Translation
Neural machine translation (NMT) becomes a new approach to machine translation and generates much more fluent results compared to statistical machine translation (SMT).
Different Contexts Lead to Different Word Embeddings
Recent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering.
