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Found 34 papers, 20 papers with code
GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification
However, the simple extension of meta-learning approaches to multi-label classification is sub-optimal for LMTC tasks due to long-tailed label distribution and coexisting of few- and zero-shot scenarios.
NJU’s submission to the WMT20 QE Shared Task
This paper describes our system of the sentence-level and word-level Quality Estimation Shared Task of WMT20.
BiTIIMT: A Bilingual Text-infilling Method for Interactive Machine Translation
In this work, we propose a novel BiTIIMT system, Bilingual Text-Infilling for Interactive Neural Machine Translation.
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification
Target-oriented multimodal sentiment classification (TMSC) is a new subtask of aspect-based sentiment analysis, which aims to determine the sentiment polarity of the opinion target mentioned in a (sentence, image) pair.
Towards Multi-label Unknown Intent Detection
In this paper, we propose a more desirable task, multi-label unknown intent detection, to detect whether the utterance contains the unknown intent, in which each utterance may contain multiple intents.
CoP: Factual Inconsistency Detection by Controlling the Preference
To separate the preference for factual consistency, we propose an unsupervised framework named CoP by controlling the preference of the generation model with the help of prompt.
What Knowledge Is Needed? Towards Explainable Memory for kNN-MT Domain Adaptation
kNN-MT presents a new paradigm for domain adaptation by building an external datastore, which usually saves all target language token occurrences in the parallel corpus.
Structure-Unified M-Tree Coding Solver for MathWord Problem
As one of the challenging NLP tasks, designing math word problem (MWP) solvers has attracted increasing research attention for the past few years.
Probing Cross-modal Semantics Alignment Capability from the Textual Perspective
In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks.
Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings
In this paper, we consider a novel research problem, music-to-text synaesthesia.
What Makes for Automatic Reconstruction of Pulmonary Segments
The automatic reconstruction of pulmonary segments by ImPulSe is accurate in metrics and visually appealing.
A Numerical Reasoning Question Answering System with Fine-grained Retriever and the Ensemble of Multiple Generators for FinQA
Here, we propose a numerical reasoning question answering system to answer numerical reasoning questions among financial text and table data sources, consisting of a retriever module, a generator module, and an ensemble module.
Exploiting Global Semantic Similarities in Knowledge Graphs by Relational Prototype Entities
By enforcing the entities' embeddings close to their associated prototypes' embeddings, our approach can effectively encourage the global semantic similarities of entities -- that can be far away in the KG -- connected by the same relation.
Analyzing the Intensity of Complaints on Social Media
Furthermore, we conduct a comprehensive linguistic analysis around complaints, including the connections between complaints and sentiment, and a cross-lingual comparison for complaints expressions used by Chinese and English speakers.
$\textit{latent}$-GLAT: Glancing at Latent Variables for Parallel Text Generation
Recently, parallel text generation has received widespread attention due to its success in generation efficiency.
ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs
To address this challenge, we propose a novel query embedding model, namely Cone Embeddings (ConE), which is the first geometry-based QE model that can handle all the FOL operations, including conjunction, disjunction, and negation.
Non-Parametric Online Learning from Human Feedback for Neural Machine Translation
We study the problem of online learning with human feedback in the human-in-the-loop machine translation, in which the human translators revise the machine-generated translations and then the corrected translations are used to improve the neural machine translation (NMT) system.
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation
Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining.
When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation
Subword segmentation algorithms have been a \textit{de facto} choice when building neural machine translation systems.
Energy-based Unknown Intent Detection with Data Manipulation
Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.
Technical Report of Team GraphMIRAcles in the WikiKG90M-LSC Track of OGB-LSC @ KDD Cup 2021
To address this challenge, we propose a framework that integrates three components -- a basic model ComplEx-CMRC, a rule miner AMIE 3, and an inference model to predict missing links.
Adaptive Nearest Neighbor Machine Translation
On four benchmark machine translation datasets, we demonstrate that the proposed method is able to effectively filter out the noises in retrieval results and significantly outperforms the vanilla kNN-MT model.
DirectQE: Direct Pretraining for Machine Translation Quality Estimation
However, we argue that there are gaps between the predictor and the estimator in both data quality and training objectives, which preclude QE models from benefiting from a large number of parallel corpora more directly.
Duplex Sequence-to-Sequence Learning for Reversible Machine Translation
Thus REDER enables reversible machine translation by simply flipping the input and output ends.
Non-Autoregressive Translation by Learning Target Categorical Codes
Non-autoregressive Transformer is a promising text generation model.
Ranked #7 on
Machine Translation
on WMT2014 German-English
Dual Side Deep Context-aware Modulation for Social Recommendation
Specifically, we first proposed a novel graph neural network to model the social relation and collaborative relation, and on top of high-order relations, a dual side deep context-aware modulation is introduced to capture the friends' information and item attraction.
Topology-Aware Correlations Between Relations for Inductive Link Prediction in Knowledge Graphs
TACT is inspired by the observation that the semantic correlation between two relations is highly correlated to their topological structure in knowledge graphs.
FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation
Previous research for adapting a general neural machine translation (NMT) model into a specific domain usually neglects the diversity in translation within the same domain, which is a core problem for domain adaptation in real-world scenarios.
Opinion Transmission Network for Jointly Improving Aspect-oriented Opinion Words Extraction and Sentiment Classification
In this paper, we propose a novel joint model, Opinion Transmission Network (OTN), to exploit the potential bridge between ALSC and AOWE to achieve the goal of facilitating them simultaneously.
Transformer-based Multi-Aspect Modeling for Multi-Aspect Multi-Sentiment Analysis
To facilitate the research of ABSA, NLPCC 2020 Shared Task 2 releases a new large-scale Multi-Aspect Multi-Sentiment (MAMS) dataset.
Rethinking Document-level Neural Machine Translation
This paper does not aim at introducing a novel model for document-level neural machine translation.
MIA-Prognosis: A Deep Learning Framework to Predict Therapy Response
Furthermore, we experiment the proposed method on an in-house, retrospective dataset of real-world non-small cell lung cancer patients under anti-PD-1 immunotherapy.
Ranked #1 on
Text-To-Speech Synthesis
on 20000 utterances
(using extra training data)
PNAT: Non-autoregressive Transformer by Position Learning
However, position modeling of output words is an essential problem in non-autoregressive text generation.
