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Found 73 papers, 14 papers with code
On the Effectiveness of Sentence Encoding for Intent Detection Meta-Learning
Recent studies on few-shot intent detection have attempted to formulate the task as a meta-learning problem, where a meta-learning model is trained with a certain capability to quickly adapt to newly specified few-shot tasks with potentially unseen intent categories.
OPERA: Operation-Pivoted Discrete Reasoning over Text
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
Jointly Learning Guidance Induction and Faithful Summary Generation via Conditional Variational Autoencoders
Abstractive summarization can generate high quality results with the development of the neural network.
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue
In the first layer, in order to extract textual features of utterances, we propose a convolutional self-attention network(CAN).
OPERA:Operation-Pivoted Discrete Reasoning over Text
To inherit the advantages of these two types of methods, we propose OPERA, an operation-pivoted discrete reasoning framework, where lightweight symbolic operations (compared with logical forms) as neural modules are utilized to facilitate the reasoning ability and interpretability.
Document-Level Relation Extraction with Sentences Importance Estimation and Focusing
Document-level relation extraction (DocRE) aims to determine the relation between two entities from a document of multiple sentences.
Ranked #3 on
Dialog Relation Extraction
on DialogRE
(F1c (v1) metric)
CLMLF:A Contrastive Learning and Multi-Layer Fusion Method for Multimodal Sentiment Detection
Compared with unimodal data, multimodal data can provide more features to help the model analyze the sentiment of data.
Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples.
Ranked #1 on
Few-shot NER
on Few-NERD (INTRA)
Responsive Listening Head Generation: A Benchmark Dataset and Baseline
Automatically synthesizing listening behavior that actively responds to a talking head, is critical to applications such as digital human, virtual agents and social robots.
A Neural Conversation Generation Model via Equivalent Shared Memory Investigation
Nevertheless, few works utilized the knowledge extracted from similar conversations for utterance generation.
EviDR: Evidence-Emphasized Discrete Reasoning for Reasoning Machine Reading Comprehension
Reasoning machine reading comprehension (R-MRC) aims to answer complex questions that require discrete reasoning based on text.
HITMI\&T at SemEval-2021 Task 5: Integrating Transformer and CRF for Toxic Spans Detection
This paper introduces our system at SemEval-2021 Task 5: Toxic Spans Detection.
Issues with Entailment-based Zero-shot Text Classification
The general format of natural language inference (NLI) makes it tempting to be used for zero-shot text classification by casting any target label into a sentence of hypothesis and verifying whether or not it could be entailed by the input, aiming at generic classification applicable on any specified label space.
Discriminative Reasoning for Document-level Relation Extraction
Document-level relation extraction (DocRE) models generally use graph networks to implicitly model the reasoning skill (i. e., pattern recognition, logical reasoning, coreference reasoning, etc.)
Ranked #21 on
Relation Extraction
on DocRED
Word Embedding Transformation for Robust Unsupervised Bilingual Lexicon Induction
Embeddings of two languages are made to match with each other by rotating and scaling.
Gumbel-Attention for Multi-modal Machine Translation
Multi-modal machine translation (MMT) improves translation quality by introducing visual information.
Ranked #3 on
Multimodal Machine Translation
on Multi30K
Demystifying Learning of Unsupervised Neural Machine Translation
Unsupervised Neural Machine Translation or UNMT has received great attention in recent years.
Document-Level Relation Extraction with Reconstruction
In document-level relation extraction (DocRE), graph structure is generally used to encode relation information in the input document to classify the relation category between each entity pair, and has greatly advanced the DocRE task over the past several years.
Ranked #31 on
Relation Extraction
on DocRED
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT
Internet memes emotion recognition is focused by many researchers.
Robust Machine Reading Comprehension by Learning Soft labels
Neural models have achieved great success on the task of machine reading comprehension (MRC), which are typically trained on hard labels.
Cross Copy Network for Dialogue Generation
In the past few years, audiences from different fields witness the achievements of sequence-to-sequence models (e. g., LSTM+attention, Pointer Generator Networks, and Transformer) to enhance dialogue content generation.
AI-lead Court Debate Case Investigation
The multi-role judicial debate composed of the plaintiff, defendant, and judge is an important part of the judicial trial.
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training
This paper aims to enhance the few-shot relation classification especially for sentences that jointly describe multiple relations.
Reliable Evaluations for Natural Language Inference based on a Unified Cross-dataset Benchmark
Recent studies show that crowd-sourced Natural Language Inference (NLI) datasets may suffer from significant biases like annotation artifacts.
End-to-End Speech Translation with Adversarial Training
End-to-End speech translation usually leverages audio-to-text parallel data to train an available speech translation model which has shown impressive results on various speech translation tasks.
Demographics Should Not Be the Reason of Toxicity: Mitigating Discrimination in Text Classifications with Instance Weighting
In this paper, we formalize the unintended biases in text classification datasets as a kind of selection bias from the non-discrimination distribution to the discrimination distribution.
Knowledge Distillation for Multilingual Unsupervised Neural Machine Translation
Unsupervised neural machine translation (UNMT) has recently achieved remarkable results for several language pairs.
Self-Training for Unsupervised Neural Machine Translation in Unbalanced Training Data Scenarios
Unsupervised neural machine translation (UNMT) that relies solely on massive monolingual corpora has achieved remarkable results in several translation tasks.
Detecting and Understanding Generalization Barriers for Neural Machine Translation
Generalization to unseen instances is our eternal pursuit for all data-driven models.
Understanding Learning Dynamics for Neural Machine Translation
Despite the great success of NMT, there still remains a severe challenge: it is hard to interpret the internal dynamics during its training process.
Look-into-Object: Self-supervised Structure Modeling for Object Recognition
Specifically, we first propose an object-extent learning module for localizing the object according to the visual patterns shared among the instances in the same category.
Ranked #1 on
Image Recognition
on ImageNet
Robust Unsupervised Neural Machine Translation with Adversarial Denoising Training
Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community.
Modeling Future Cost for Neural Machine Translation
Existing neural machine translation (NMT) systems utilize sequence-to-sequence neural networks to generate target translation word by word, and then make the generated word at each time-step and the counterpart in the references as consistent as possible.
Multimodal Matching Transformer for Live Commenting
In this work, we aim to improve the relevance between live comments and videos by modeling the cross-modal interactions among different modalities.
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization
Many Data Augmentation (DA) methods have been proposed for neural machine translation.
Mitigating Annotation Artifacts in Natural Language Inference Datasets to Improve Cross-dataset Generalization Ability
This irregularity makes the evaluation results over-estimated and affects models' generalization ability.
Duality Regularization for Unsupervised Bilingual Lexicon Induction
Unsupervised bilingual lexicon induction naturally exhibits duality, which results from symmetry in back-translation.
Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation
However, it has not been well-studied for unsupervised neural machine translation (UNMT), although UNMT has recently achieved remarkable results in several domain-specific language pairs.
Sentence-Level Agreement for Neural Machine Translation
The training objective of neural machine translation (NMT) is to minimize the loss between the words in the translated sentences and those in the references.
Unsupervised Bilingual Word Embedding Agreement for Unsupervised Neural Machine Translation
In previous methods, UBWE is first trained using non-parallel monolingual corpora and then this pre-trained UBWE is used to initialize the word embedding in the encoder and decoder of UNMT.
Improving Neural Machine Translation with Neural Syntactic Distance
The explicit use of syntactic information has been proved useful for neural machine translation (NMT).
Understanding and Improving Hidden Representations for Neural Machine Translation
Multilayer architectures are currently the gold standard for large-scale neural machine translation.
CN-HIT-MI.T at SemEval-2019 Task 6: Offensive Language Identification Based on BiLSTM with Double Attention
Our macro-averaged F1-score in sub-task A is 0. 768, ranking 28/103.
Selection Bias Explorations and Debias Methods for Natural Language Sentence Matching Datasets
Natural Language Sentence Matching (NLSM) has gained substantial attention from both academics and the industry, and rich public datasets contribute a lot to this process.
Deep Attention Neural Tensor Network for Visual Question Answering
First, we model one of the pairwise interaction (e. g., image and question) by bilinear features, which is further encoded with the third dimension (e. g., answer) to be a triplet by bilinear tensor product.
Neural Document Summarization by Jointly Learning to Score and Select Sentences
In this paper, we present a novel end-to-end neural network framework for extractive document summarization by jointly learning to score and select sentences.
Ranked #8 on
Extractive Text Summarization
on CNN / Daily Mail
Forest-Based Neural Machine Translation
Tree-based neural machine translation (NMT) approaches, although achieved impressive performance, suffer from a major drawback: they only use the 1-best parse tree to direct the translation, which potentially introduces translation mistakes due to parsing errors.
Table-to-Text: Describing Table Region with Natural Language
The model maps a row from a table to a continuous vector and then generates a natural language sentence by leveraging the semantics of a table.
Syntax-Directed Attention for Neural Machine Translation
In this paper, we extend local attention with syntax-distance constraint, to focus on syntactically related source words with the predicted target word, thus learning a more effective context vector for word prediction.
Context-Aware Smoothing for Neural Machine Translation
In Neural Machine Translation (NMT), each word is represented as a low-dimension, real-value vector for encoding its syntax and semantic information.
Neural Machine Translation with Source Dependency Representation
Source dependency information has been successfully introduced into statistical machine translation.
Automatic Dataset Augmentation
Large scale image dataset and deep convolutional neural network (DCNN) are two primary driving forces for the rapid progress made in generic object recognition tasks in recent years.
Constraint-Based Question Answering with Knowledge Graph
WebQuestions and SimpleQuestions are two benchmark data-sets commonly used in recent knowledge-based question answering (KBQA) work.
A Distribution-based Model to Learn Bilingual Word Embeddings
We introduce a distribution based model to learn bilingual word embeddings from monolingual data.
Building A Case-based Semantic English-Chinese Parallel Treebank
This Treebank is a part of a semantic corpus building project, which aims to build a semantic bilingual corpus annotated with syntactic, semantic cases and word senses.
Augmenting Phrase Table by Employing Lexicons for Pivot-based SMT
Pivot language is employed as a way to solve the data sparseness problem in machine translation, especially when the data for a particular language pair does not exist.
Learning High-level Image Representation for Image Retrieval via Multi-Task DNN using Clickthrough Data
Image retrieval refers to finding relevant images from an image database for a query, which is considered difficult for the gap between low-level representation of images and high-level representation of queries.
