1 |
Learning Disentangled Semantic Representations for Zero-Shot Cross-Lingual Transfer in Multilingual Machine Reading Comprehension ...
|
|
|
|
BASE
|
|
Show details
|
|
2 |
CogAlign: Learning to Align Textual Neural Representations to Cognitive Language Processing Signals ...
|
|
|
|
BASE
|
|
Show details
|
|
3 |
AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
4 |
Multi-Head Highly Parallelized LSTM Decoder for Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
5 |
TGEA: An Error-Annotated Dataset and Benchmark Tasks for TextGeneration from Pretrained Language Models ...
|
|
|
|
BASE
|
|
Show details
|
|
6 |
Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
7 |
Bridging between Cognitive Processing Signals and Linguistic Features via a Unified Attentional Network ...
|
|
|
|
BASE
|
|
Show details
|
|
8 |
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context ...
|
|
|
|
BASE
|
|
Show details
|
|
9 |
RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling ...
|
|
|
|
BASE
|
|
Show details
|
|
10 |
Probing Word Translations in the Transformer and Trading Decoder for Encoder Layers ...
|
|
|
|
BASE
|
|
Show details
|
|
11 |
Modeling Homophone Noise for Robust Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
12 |
Merging External Bilingual Pairs into Neural Machine Translation ...
|
|
|
|
BASE
|
|
Show details
|
|
13 |
BiPaR: A Bilingual Parallel Dataset for Multilingual and Cross-lingual Reading Comprehension on Novels ...
|
|
|
|
BASE
|
|
Show details
|
|
14 |
Fusing Recency into Neural Machine Translation with an Inter-Sentence Gate Model ...
|
|
|
|
BASE
|
|
Show details
|
|
15 |
Modeling Source Syntax for Neural Machine Translation ...
|
|
|
|
Abstract:
Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, ... : Accepted by ACL 2017 ...
|
|
Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
|
|
URL: https://arxiv.org/abs/1705.01020 https://dx.doi.org/10.48550/arxiv.1705.01020
|
|
BASE
|
|
Hide details
|
|
17 |
BattRAE: Bidimensional Attention-Based Recursive Autoencoders for Learning Bilingual Phrase Embeddings ...
|
|
|
|
BASE
|
|
Show details
|
|
19 |
Combining documentation and research: ongoing work on an endangered language
|
|
|
|
In: Proceedings of IALP 2012 (2012 International Conference on Asian Language Processing) ; IALP 2012 (2012 International Conference on Asian Language Processing) ; https://halshs.archives-ouvertes.fr/halshs-00731261 ; IALP 2012 (2012 International Conference on Asian Language Processing), 2012, Hanoi, Vietnam. pp.169-172 (2012)
|
|
BASE
|
|
Show details
|
|
20 |
Combining documentation and research: ongoing work on an endangered language
|
|
|
|
In: Proceedings of IALP 2012 (2012 International Conference on Asian Language Processing) ; IALP 2012 (2012 International Conference on Asian Language Processing) ; https://halshs.archives-ouvertes.fr/halshs-00731261 ; IALP 2012 (2012 International Conference on Asian Language Processing), 2012, Hanoi, Vietnam. pp.169-172 (2012)
|
|
BASE
|
|
Show details
|
|
|
|