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Found 13 papers, 6 papers with code
Finding Sub-task Structure with Natural Language Instruction
When mapping a natural language instruction to a sequence of actions, it is often useful toidentify sub-tasks in the instruction.
The University of Tokyo’s Submissions to the WAT 2020 Shared Task
The paper describes the development process of the The University of Tokyo’s NMT systems that were submitted to the WAT 2020 Document-level Business Scene Dialogue Translation sub-task.
LEIA: Facilitating Cross-Lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
In this study, we introduce LEIA, a language adaptation tuning method that utilizes Wikipedia entity names aligned across languages.
Emergent Communication with Attention
To develop computational agents that better communicate using their own emergent language, we endow the agents with an ability to focus their attention on particular concepts in the environment.
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.
Pretraining with Artificial Language: Studying Transferable Knowledge in Language Models
We investigate what kind of structural knowledge learned in neural network encoders is transferable to processing natural language.
mLUKE: The Power of Entity Representations in Multilingual Pretrained Language Models
We train a multilingual language model with 24 languages with entity representations and show the model consistently outperforms word-based pretrained models in various cross-lingual transfer tasks.
Ranked #1 on
Cross-Lingual Question Answering
on XQuAD
(Average F1 metric, using extra
training data)
Zero-pronoun Data Augmentation for Japanese-to-English Translation
For Japanese-to-English translation, zero pronouns in Japanese pose a challenge, since the model needs to infer and produce the corresponding pronoun in the target side of the English sentence.
Document-aligned Japanese-English Conversation Parallel Corpus
Sentence-level (SL) machine translation (MT) has reached acceptable quality for many high-resourced languages, but not document-level (DL) MT, which is difficult to 1) train with little amount of DL data; and 2) evaluate, as the main methods and data sets focus on SL evaluation.
Designing the Business Conversation Corpus
While the progress of machine translation of written text has come far in the past several years thanks to the increasing availability of parallel corpora and corpora-based training technologies, automatic translation of spoken text and dialogues remains challenging even for modern systems.
Ranked #1 on
Machine Translation
on Business Scene Dialogue JA-EN
(using extra training data)
Data Augmentation with Unsupervised Machine Translation Improves the Structural Similarity of Cross-lingual Word Embeddings
Unsupervised cross-lingual word embedding (CLWE) methods learn a linear transformation matrix that maps two monolingual embedding spaces that are separately trained with monolingual corpora.
Revisiting the Context Window for Cross-lingual Word Embeddings
Existing approaches to mapping-based cross-lingual word embeddings are based on the assumption that the source and target embedding spaces are structurally similar.
Bilingual Lexicon Induction
Cross-Lingual Word Embeddings
+1
