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Found 71 papers, 10 papers with code
CASIA's System for IWSLT 2020 Open Domain Translation
This paper describes the CASIA{'}s system for the IWSLT 2020 open domain translation task.
Improving Autoregressive NMT with Non-Autoregressive Model
In this work, we propose a novel Encoder-NAD-AD framework for NMT, aiming at boosting AT with global information produced by NAT model.
Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization
Cross-lingual summarization aims at summarizing a document in one language (e. g., Chinese) into another language (e. g., English).
Neural Machine Translation: Challenges, Progress and Future
Machine translation (MT) is a technique that leverages computers to translate human languages automatically.
Synchronous Speech Recognition and Speech-to-Text Translation with Interactive Decoding
Speech-to-text translation (ST), which translates source language speech into target language text, has attracted intensive attention in recent years.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Attribute-aware Sequence Network for Review Summarization
Review summarization aims to generate a condensed summary for a review or multiple reviews.
Synchronously Generating Two Languages with Interactive Decoding
In this paper, we introduce a novel interactive approach to translate a source language into two different languages simultaneously and interactively.
NCLS: Neural Cross-Lingual Summarization
Moreover, we propose to further improve NCLS by incorporating two related tasks, monolingual summarization and machine translation, into the training process of CLS under multi-task learning.
Are You for Real? Detecting Identity Fraud via Dialogue Interactions
In this paper, we focus on identity fraud detection in loan applications and propose to solve this problem with a novel interactive dialogue system which consists of two modules.
Understanding Memory Modules on Learning Simple Algorithms
Recent work has shown that memory modules are crucial for the generalization ability of neural networks on learning simple algorithms.
A Compact and Language-Sensitive Multilingual Translation Method
We verify our methods on various translation scenarios, including one-to-many, many-to-many and zero-shot.
Sequence Generation: From Both Sides to the Middle
The encoder-decoder framework has achieved promising process for many sequence generation tasks, such as neural machine translation and text summarization.
Incremental Learning from Scratch for Task-Oriented Dialogue Systems
Clarifying user needs is essential for existing task-oriented dialogue systems.
Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference
Dialogue contexts are proven helpful in the spoken language understanding (SLU) system and they are typically encoded with explicit memory representations.
Synchronous Bidirectional Neural Machine Translation
In this paper, we introduce a synchronous bidirectional neural machine translation (SB-NMT) that predicts its outputs using left-to-right and right-to-left decoding simultaneously and interactively, in order to leverage both of the history and future information at the same time.
Ranked #28 on
Machine Translation
on WMT2014 English-German
End-to-End Speech Translation with Knowledge Distillation
End-to-end speech translation (ST), which directly translates from source language speech into target language text, has attracted intensive attentions in recent years.
Synchronous Bidirectional Inference for Neural Sequence Generation
In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively.
Language-Independent Representor for Neural Machine Translation
Current Neural Machine Translation (NMT) employs a language-specific encoder to represent the source sentence and adopts a language-specific decoder to generate target translation.
Three Strategies to Improve One-to-Many Multilingual Translation
However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models.
Memory, Show the Way: Memory Based Few Shot Word Representation Learning
Distributional semantic models (DSMs) generally require sufficient examples for a word to learn a high quality representation.
Addressing Troublesome Words in Neural Machine Translation
One of the weaknesses of Neural Machine Translation (NMT) is in handling lowfrequency and ambiguous words, which we refer as troublesome words.
MSMO: Multimodal Summarization with Multimodal Output
In this paper, we propose a novel task, multimodal summarization with multimodal output (MSMO).
Associative Multichannel Autoencoder for Multimodal Word Representation
In this paper we address the problem of learning multimodal word representations by integrating textual, visual and auditory inputs.
A Teacher-Student Framework for Maintainable Dialog Manager
Specifically, the {``}student{''} is an extended dialog manager based on a new ontology, and the {``}teacher{''} is existing resources used for guiding the learning process of the {``}student{''}.
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
This paper focuses on the language transferring task given a tiny in-domain parallel SLU corpus.
Cultural Vocal Bursts Intensity Prediction
domain classification
+6
Adopting the Word-Pair-Dependency-Triplets with Individual Comparison for Natural Language Inference
This paper proposes to perform natural language inference with Word-Pair-Dependency-Triplets.
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language
An SLU corpus is a monolingual corpus with domain/intent/slot labels.
Cultural Vocal Bursts Intensity Prediction
domain classification
+8
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings
The document representation is combined with user and overall rating information to predict aspect ratings of a review.
Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization
In this paper, we investigate the sentence summarization task that produces a summary from a source sentence.
Ranked #7 on
Text Summarization
on DUC 2004 Task 1
Phrase Table as Recommendation Memory for Neural Machine Translation
Neural Machine Translation (NMT) has drawn much attention due to its promising translation performance recently.
Learning Multimodal Word Representation via Dynamic Fusion Methods
Multimodal models have been proven to outperform text-based models on learning semantic word representations.
Learning from Parenthetical Sentences for Term Translation in Machine Translation
Terms extensively exist in specific domains, and term translation plays a critical role in domain-specific machine translation (MT) tasks.
Investigating Inner Properties of Multimodal Representation and Semantic Compositionality with Brain-based Componential Semantics
Considering that multimodal models are originally motivated by human concept representations, we assume that correlating multimodal representations with brain-based semantics would interpret their inner properties to answer the above questions.
Learning Semantic Representations
Natural Language Understanding
Word, Subword or Character? An Empirical Study of Granularity in Chinese-English NMT
Our experiments show that subword model performs best for Chinese-to-English translation with the vocabulary which is not so big while hybrid word-character model is most suitable for English-to-Chinese translation.
Towards Neural Machine Translation with Partially Aligned Corpora
While neural machine translation (NMT) has become the new paradigm, the parameter optimization requires large-scale parallel data which is scarce in many domains and language pairs.
Multi-modal Summarization for Asynchronous Collection of Text, Image, Audio and Video
In this work, we propose an extractive Multi-modal Summarization (MMS) method which can automatically generate a textual summary given a set of documents, images, audios and videos related to a specific topic.
Automatic Speech Recognition (ASR)
Document Summarization
+1
Exploiting Word Internal Structures for Generic Chinese Sentence Representation
We introduce a novel mixed characterword architecture to improve Chinese sentence representations, by utilizing rich semantic information of word internal structures.
Look-ahead Attention for Generation in Neural Machine Translation
The attention model has become a standard component in neural machine translation (NMT) and it guides translation process by selectively focusing on parts of the source sentence when predicting each target word.
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).
Shortcut Sequence Tagging
To simply the stacked architecture, we propose a framework called shortcut block, which is a marriage of the gating mechanism and shortcuts, while discarding the self-connected part in LSTM cell.
Bridging Neural Machine Translation and Bilingual Dictionaries
Neural Machine Translation (NMT) has become the new state-of-the-art in several language pairs.
An Empirical Exploration of Skip Connections for Sequential Tagging
In this paper, we empirically explore the effects of various kinds of skip connections in stacked bidirectional LSTMs for sequential tagging.
A Dynamic Window Neural Network for CCG Supertagging
These motivate us to build a supertagger with a dynamic window approach, which can be treated as an attention mechanism on the local contexts.
Learning Sentence Representation with Guidance of Human Attention
Recently, much progress has been made in learning general-purpose sentence representations that can be used across domains.
One Sentence One Model for Neural Machine Translation
Neural machine translation (NMT) becomes a new state-of-the-art and achieves promising translation results using a simple encoder-decoder neural network.
Neural Name Translation Improves Neural Machine Translation
In order to control computational complexity, neural machine translation (NMT) systems convert all rare words outside the vocabulary into a single unk symbol.
A Bilingual Discourse Corpus and Its Applications
Existing discourse research only focuses on the monolingual languages and the inconsistency between languages limits the power of the discourse theory in multilingual applications such as machine translation.
Beyond Word-based Language Model in Statistical Machine Translation
Language model is one of the most important modules in statistical machine translation and currently the word-based language model dominants this community.
Domain Adaptation for Syntactic and Semantic Dependency Parsing Using Deep Belief Networks
Experiments on English data in the CoNLL 2009 shared task show that our method largely reduced the performance drop on out-of-domain test data.
Large-scale Word Alignment Using Soft Dependency Cohesion Constraints
In this paper, we take dependency cohesion as a soft constraint, and integrate it into a generative model for large-scale word alignment experiments.
Unsupervised Tree Induction for Tree-based Translation
In current research, most tree-based translation models are built directly from parse trees.
