15,368 research outputs found
Neural Machine Translation by Generating Multiple Linguistic Factors
Factored neural machine translation (FNMT) is founded on the idea of using
the morphological and grammatical decomposition of the words (factors) at the
output side of the neural network. This architecture addresses two well-known
problems occurring in MT, namely the size of target language vocabulary and the
number of unknown tokens produced in the translation. FNMT system is designed
to manage larger vocabulary and reduce the training time (for systems with
equivalent target language vocabulary size). Moreover, we can produce
grammatically correct words that are not part of the vocabulary. FNMT model is
evaluated on IWSLT'15 English to French task and compared to the baseline
word-based and BPE-based NMT systems. Promising qualitative and quantitative
results (in terms of BLEU and METEOR) are reported.Comment: 11 pages, 3 figues, SLSP conferenc
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
Translating Phrases in Neural Machine Translation
Phrases play an important role in natural language understanding and machine
translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is
difficult to integrate them into current neural machine translation (NMT) which
reads and generates sentences word by word. In this work, we propose a method
to translate phrases in NMT by integrating a phrase memory storing target
phrases from a phrase-based statistical machine translation (SMT) system into
the encoder-decoder architecture of NMT. At each decoding step, the phrase
memory is first re-written by the SMT model, which dynamically generates
relevant target phrases with contextual information provided by the NMT model.
Then the proposed model reads the phrase memory to make probability estimations
for all phrases in the phrase memory. If phrase generation is carried on, the
NMT decoder selects an appropriate phrase from the memory to perform phrase
translation and updates its decoding state by consuming the words in the
selected phrase. Otherwise, the NMT decoder generates a word from the
vocabulary as the general NMT decoder does. Experiment results on the Chinese
to English translation show that the proposed model achieves significant
improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201
Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop
The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques
specifically developed for analyzing and understanding the inner-workings and
representations acquired by neural models of language. Approaches included:
systematic manipulation of input to neural networks and investigating the
impact on their performance, testing whether interpretable knowledge can be
decoded from intermediate representations acquired by neural networks,
proposing modifications to neural network architectures to make their knowledge
state or generated output more explainable, and examining the performance of
networks on simplified or formal languages. Here we review a number of
representative studies in each category
Learning Discourse-level Diversity for Neural Dialog Models using Conditional Variational Autoencoders
While recent neural encoder-decoder models have shown great promise in
modeling open-domain conversations, they often generate dull and generic
responses. Unlike past work that has focused on diversifying the output of the
decoder at word-level to alleviate this problem, we present a novel framework
based on conditional variational autoencoders that captures the discourse-level
diversity in the encoder. Our model uses latent variables to learn a
distribution over potential conversational intents and generates diverse
responses using only greedy decoders. We have further developed a novel variant
that is integrated with linguistic prior knowledge for better performance.
Finally, the training procedure is improved by introducing a bag-of-word loss.
Our proposed models have been validated to generate significantly more diverse
responses than baseline approaches and exhibit competence in discourse-level
decision-making.Comment: Appeared in ACL2017 proceedings as a long paper. Correct a
calculation mistake in Table 1 E-bow & A-bow and results into higher score
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
Natural language generation (NLG) is a critical component in spoken dialogue
system, which can be divided into two phases: (1) sentence planning: deciding
the overall sentence structure, (2) surface realization: determining specific
word forms and flattening the sentence structure into a string. With the rise
of deep learning, most modern NLG models are based on a sequence-to-sequence
(seq2seq) model, which basically contains an encoder-decoder structure; these
NLG models generate sentences from scratch by jointly optimizing sentence
planning and surface realization. However, such simple encoder-decoder
architecture usually fail to generate complex and long sentences, because the
decoder has difficulty learning all grammar and diction knowledge well. This
paper introduces an NLG model with a hierarchical attentional decoder, where
the hierarchy focuses on leveraging linguistic knowledge in a specific order.
The experiments show that the proposed method significantly outperforms the
traditional seq2seq model with a smaller model size, and the design of the
hierarchical attentional decoder can be applied to various NLG systems.
Furthermore, different generation strategies based on linguistic patterns are
investigated and analyzed in order to guide future NLG research work.Comment: accepted by the 7th IEEE Workshop on Spoken Language Technology (SLT
2018). arXiv admin note: text overlap with arXiv:1808.0274
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