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Found 83 papers, 15 papers with code
Findings of the 2021 Conference on Machine Translation (WMT21)
This paper presents the results of the newstranslation task, the multilingual low-resourcetranslation for Indo-European languages, thetriangular translation task, and the automaticpost-editing task organised as part of the Con-ference on Machine Translation (WMT) 2021. In the news task, participants were asked tobuild machine translation systems for any of10 language pairs, to be evaluated on test setsconsisting mainly of news stories.
DiDi’s Machine Translation System for WMT2020
This paper describes the DiDi AI Labs’ submission to the WMT2020 news translation shared task.
Development and Validation of a Machine Learning Algorithm for Clinical Wellness Visit Classification in Cats and Dogs
The algorithm demonstrated a specificity of 0. 94 (95% CI: 0. 91 to 0. 96), implying its accuracy in distinguishing non-wellness visits.
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play
Task-oriented dialog systems are often trained on human/human dialogs, such as collected from Wizard-of-Oz interfaces.
MeetDot: Videoconferencing with Live Translation Captions
We use the re-translation strategy to translate the streamed speech, resulting in caption flicker.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Learning Mathematical Properties of Integers
Embedding words in high-dimensional vector spaces has proven valuable in many natural language applications.
A Hybrid Task-Oriented Dialog System with Domain and Task Adaptive Pretraining
This paper describes our submission for the End-to-end Multi-domain Task Completion Dialog shared task at the 9th Dialog System Technology Challenge (DSTC-9).
Why Neural Machine Translation Prefers Empty Outputs
Using different EoS types in target sentences of different lengths exposes and eliminates this implicit smoothing.
MUSE: Textual Attributes Guided Portrait Painting Generation
Experiments show that our approach can accurately illustrate 78% textual attributes, which also help MUSE capture the subject in a more creative and expressive way.
DiDi's Machine Translation System for WMT2020
This paper describes DiDi AI Labs' submission to the WMT2020 news translation shared task.
ReviewRobot: Explainable Paper Review Generation based on Knowledge Synthesis
To assist human review process, we build a novel ReviewRobot to automatically assign a review score and write comments for multiple categories such as novelty and meaningful comparison.
Learning to Pronounce Chinese Without a Pronunciation Dictionary
We demonstrate a program that learns to pronounce Chinese text in Mandarin, without a pronunciation dictionary.
Solving Historical Dictionary Codes with a Neural Language Model
We solve difficult word-based substitution codes by constructing a decoding lattice and searching that lattice with a neural language model.
MEEP: An Open-Source Platform for Human-Human Dialog Collection and End-to-End Agent Training
We create a new task-oriented dialog platform (MEEP) where agents are given considerable freedom in terms of utterances and API calls, but are constrained to work within a push-button environment.
FINDINGS OF THE IWSLT 2020 EVALUATION CAMPAIGN
The evaluation campaign of the International Conference on Spoken Language Translation (IWSLT 2020) featured this year six challenge tracks: (i) Simultaneous speech translation, (ii) Video speech translation, (iii) Offline speech translation, (iv) Conversational speech translation, (v) Open domain translation, and (vi) Non-native speech translation.
Parallel Corpus Filtering via Pre-trained Language Models
Web-crawled data provides a good source of parallel corpora for training machine translation models.
Ranked #1 on
Machine Translation
on WMT2019 English-Japanese
PaperRobot: Incremental Draft Generation of Scientific Ideas
We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper.
Plan-And-Write: Towards Better Automatic Storytelling
Automatic storytelling is challenging since it requires generating long, coherent natural language to describes a sensible sequence of events.
Decipherment of Historical Manuscript Images
European libraries and archives are filled with enciphered manuscripts from the early modern period.
Describing a Knowledge Base
We aim to automatically generate natural language descriptions about an input structured knowledge base (KB).
Augmenting Statistical Machine Translation with Subword Translation of Out-of-Vocabulary Words
Most statistical machine translation systems cannot translate words that are unseen in the training data.
Creative Language Encoding under Censorship
People often create obfuscated language for online communication to avoid Internet censorship, share sensitive information, express strong sentiment or emotion, plan for secret actions, trade illegal products, or simply hold interesting conversations.
AMR Beyond the Sentence: the Multi-sentence AMR corpus
There are few corpora that endeavor to represent the semantic content of entire documents.
Translating a Language You Don't Know In the Chinese Room
In a corruption of John Searle{'}s famous AI thought experiment, the Chinese Room (Searle, 1980), we twist its original intent by enabling humans to translate text, e. g. from Uyghur to English, even if they don{'}t have any prior knowledge of the source language.
Out-of-the-box Universal Romanization Tool uroman
We present uroman, a tool for converting text in myriads of languages and scripts such as Chinese, Arabic and Cyrillic into a common Latin-script representation.
Fast Locality Sensitive Hashing for Beam Search on GPU
We present a GPU-based Locality Sensitive Hashing (LSH) algorithm to speed up beam search for sequence models.
ELISA-EDL: A Cross-lingual Entity Extraction, Linking and Localization System
We demonstrate ELISA-EDL, a state-of-the-art re-trainable system to extract entity mentions from low-resource languages, link them to external English knowledge bases, and visualize locations related to disaster topics on a world heatmap.
Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation
We present a method for improving word alignments using word similarities.
Towards Controllable Story Generation
We present a general framework of analyzing existing story corpora to generate controllable and creative new stories.
Modeling Naive Psychology of Characters in Simple Commonsense Stories
Understanding a narrative requires reading between the lines and reasoning about the unspoken but obvious implications about events and people's mental states - a capability that is trivial for humans but remarkably hard for machines.
Ranked #2 on
Emotion Classification
on ROCStories
Paper Abstract Writing through Editing Mechanism
We present a paper abstract writing system based on an attentive neural sequence-to-sequence model that can take a title as input and automatically generate an abstract.
Ranked #1 on
Paper generation
on ACL Title and Abstract Dataset
Multi-lingual Common Semantic Space Construction via Cluster-consistent Word Embedding
We construct a multilingual common semantic space based on distributional semantics, where words from multiple languages are projected into a shared space to enable knowledge and resource transfer across languages.
Learning from Past Mistakes: Improving Automatic Speech Recognition Output via Noisy-Clean Phrase Context Modeling
In this work we model ASR as a phrase-based noisy transformation channel and propose an error correction system that can learn from the aggregate errors of all the independent modules constituting the ASR and attempt to invert those.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Recurrent Neural Networks as Weighted Language Recognizers
We investigate the computational complexity of various problems for simple recurrent neural networks (RNNs) as formal models for recognizing weighted languages.
Embracing Non-Traditional Linguistic Resources for Low-resource Language Name Tagging
Current supervised name tagging approaches are inadequate for most low-resource languages due to the lack of annotated data and actionable linguistic knowledge.
Deciphering Related Languages
We present a method for translating texts between close language pairs.
Biomedical Event Extraction using Abstract Meaning Representation
We propose a novel, Abstract Meaning Representation (AMR) based approach to identifying molecular events/interactions in biomedical text.
Speeding Up Neural Machine Translation Decoding by Shrinking Run-time Vocabulary
Compared with Locality Sensitive Hashing (LSH), decoding with word alignments is GPU-friendly, orthogonal to existing speedup methods and more robust across language pairs.
Cross-lingual Name Tagging and Linking for 282 Languages
The ambitious goal of this work is to develop a cross-lingual name tagging and linking framework for 282 languages that exist in Wikipedia.
Unsupervised Neural Hidden Markov Models
In this work, we present the first results for neuralizing an Unsupervised Hidden Markov Model.
Extracting Structured Scholarly Information from the Machine Translation Literature
Understanding the experimental results of a scientific paper is crucial to understanding its contribution and to comparing it with related work.
Transfer Learning for Low-Resource Neural Machine Translation
Ensembling and unknown word replacement add another 2 Bleu which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair.
Multi-Source Neural Translation
We build a multi-source machine translation model and train it to maximize the probability of a target English string given French and German sources.
Using Syntax-Based Machine Translation to Parse English into Abstract Meaning Representation
To make this work, we transform the AMR structure into a form suitable for the mechanics of SBMT and useful for modeling.
Mapping Between English Strings and Reentrant Semantic Graphs
We investigate formalisms for capturing the relation between semantic graphs and English strings.
