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Found 22 papers, 1 papers with code
Vision-and-Language Pretrained Models: A Survey
Pretrained models have produced great success in both Computer Vision (CV) and Natural Language Processing (NLP).
Sequential Attention Module for Natural Language Processing
In this paper, we target at how to further improve the token representations on the language models.
RefBERT: Compressing BERT by Referencing to Pre-computed Representations
Differently, in this paper, we propose RefBERT to leverage the knowledge learned from the teacher, i. e., facilitating the pre-computed BERT representation on the reference sample and compressing BERT into a smaller student model.
Progressive Open-Domain Response Generation with Multiple Controllable Attributes
More specifically, PHED deploys Conditional Variational AutoEncoder (CVAE) on Transformer to include one aspect of attributes at one stage.
Sattiy at SemEval-2021 Task 9: An Ensemble Solution for Statement Verification and Evidence Finding with Tables
Question answering from semi-structured tables can be seen as a semantic parsing task and is significant and practical for pushing the boundary of natural language understanding.
PALI at SemEval-2021 Task 2: Fine-Tune XLM-RoBERTa for Word in Context Disambiguation
Second, we construct a new vector on the fine-tuned embeddings from XLM-RoBERTa and feed it to a fully-connected network to output the probability of whether the target word in the context has the same meaning or not.
MagicPai at SemEval-2021 Task 7: Method for Detecting and Rating Humor Based on Multi-Task Adversarial Training
This paper describes MagicPai's system for SemEval 2021 Task 7, HaHackathon: Detecting and Rating Humor and Offense.
Emotion Dynamics Modeling via BERT
Emotion dynamics modeling is a significant task in emotion recognition in conversation.
KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph
Entity synonyms discovery is crucial for entity-leveraging applications.
Automatic Intent-Slot Induction for Dialogue Systems
Automatically and accurately identifying user intents and filling the associated slots from their spoken language are critical to the success of dialogue systems.
UNIXLONG at SemEval-2020 Task 6: A Joint Model for Definition Extraction
The goal of this task is to extract definition, word level BIO tags and relations.
FPAI at SemEval-2020 Task 10: A Query Enhanced Model with RoBERTa for Emphasis Selection
This paper describes the model we apply in the SemEval-2020 Task 10.
Making Online Sketching Hashing Even Faster
Data-dependent hashing methods have demonstrated good performance in various machine learning applications to learn a low-dimensional representation from the original data.
Block-term Tensor Neural Networks
Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e. g., image classification, natural language processing, etc.
Effective Data-aware Covariance Estimator from Compressed Data
Estimating covariance matrix from massive high-dimensional and distributed data is significant for various real-world applications.
Hierarchical Context Enhanced Multi-Domain Dialogue System for Multi-domain Task Completion
Task 1 of the DSTC8-track1 challenge aims to develop an end-to-end multi-domain dialogue system to accomplish complex users' goals under tourist information desk settings.
BERT Meets Chinese Word Segmentation
Chinese word segmentation (CWS) is a fundamental task for Chinese language understanding.
HiGRU: Hierarchical Gated Recurrent Units for Utterance-level Emotion Recognition
In this paper, we address three challenges in utterance-level emotion recognition in dialogue systems: (1) the same word can deliver different emotions in different contexts; (2) some emotions are rarely seen in general dialogues; (3) long-range contextual information is hard to be effectively captured.
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues
The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification.
BT-Nets: Simplifying Deep Neural Networks via Block Term Decomposition
Recently, deep neural networks (DNNs) have been regarded as the state-of-the-art classification methods in a wide range of applications, especially in image classification.
A deep learning approach for predicting the quality of online health expert question-answering services
Finally, a deep belief network (DBN)-based HQA answer quality prediction framework is proposed to predict the quality of answers by learning the high-level hidden semantic representation from the physicians' answers.
Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee
Previously, oblivious random projection based approaches that project high dimensional features onto a random subspace have been used in practice for tackling high-dimensionality challenge in machine learning.
