13,250 research outputs found
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
Hierarchically Structured Reinforcement Learning for Topically Coherent Visual Story Generation
We propose a hierarchically structured reinforcement learning approach to
address the challenges of planning for generating coherent multi-sentence
stories for the visual storytelling task. Within our framework, the task of
generating a story given a sequence of images is divided across a two-level
hierarchical decoder. The high-level decoder constructs a plan by generating a
semantic concept (i.e., topic) for each image in sequence. The low-level
decoder generates a sentence for each image using a semantic compositional
network, which effectively grounds the sentence generation conditioned on the
topic. The two decoders are jointly trained end-to-end using reinforcement
learning. We evaluate our model on the visual storytelling (VIST) dataset.
Empirical results from both automatic and human evaluations demonstrate that
the proposed hierarchically structured reinforced training achieves
significantly better performance compared to a strong flat deep reinforcement
learning baseline.Comment: Accepted to AAAI 201
Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
We investigate the task of building open domain, conversational dialogue
systems based on large dialogue corpora using generative models. Generative
models produce system responses that are autonomously generated word-by-word,
opening up the possibility for realistic, flexible interactions. In support of
this goal, we extend the recently proposed hierarchical recurrent
encoder-decoder neural network to the dialogue domain, and demonstrate that
this model is competitive with state-of-the-art neural language models and
back-off n-gram models. We investigate the limitations of this and similar
approaches, and show how its performance can be improved by bootstrapping the
learning from a larger question-answer pair corpus and from pretrained word
embeddings.Comment: 8 pages with references; Published in AAAI 2016 (Special Track on
Cognitive Systems
Introduction to the special issue on cross-language algorithms and applications
With the increasingly global nature of our everyday interactions, the need for multilingual technologies to support efficient and efective information access and communication cannot be overemphasized. Computational modeling of language has been the focus of
Natural Language Processing, a subdiscipline of Artificial Intelligence. One of the current challenges for this discipline is to design methodologies and algorithms that are cross-language in order to create multilingual technologies rapidly. The goal of this JAIR special
issue on Cross-Language Algorithms and Applications (CLAA) is to present leading research in this area, with emphasis on developing unifying themes that could lead to the development of the science of multi- and cross-lingualism. In this introduction, we provide the reader with the motivation for this special issue and summarize the contributions of the papers that have been included. The selected papers cover a broad range of cross-lingual technologies including machine translation, domain and language adaptation for sentiment
analysis, cross-language lexical resources, dependency parsing, information retrieval and knowledge representation. We anticipate that this special issue will serve as an invaluable resource for researchers interested in topics of cross-lingual natural language processing.Postprint (published version
Read, Watch, and Move: Reinforcement Learning for Temporally Grounding Natural Language Descriptions in Videos
The task of video grounding, which temporally localizes a natural language
description in a video, plays an important role in understanding videos.
Existing studies have adopted strategies of sliding window over the entire
video or exhaustively ranking all possible clip-sentence pairs in a
pre-segmented video, which inevitably suffer from exhaustively enumerated
candidates. To alleviate this problem, we formulate this task as a problem of
sequential decision making by learning an agent which regulates the temporal
grounding boundaries progressively based on its policy. Specifically, we
propose a reinforcement learning based framework improved by multi-task
learning and it shows steady performance gains by considering additional
supervised boundary information during training. Our proposed framework
achieves state-of-the-art performance on ActivityNet'18 DenseCaption dataset
and Charades-STA dataset while observing only 10 or less clips per video.Comment: AAAI 201
Self-Adaptive Hierarchical Sentence Model
The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases and then to sentences through recursive gated local
composition of adjacent segments. We design a competitive mechanism (through
gating networks) to allow the representations of the same sentence to be
engaged in a particular learning task (e.g., classification), therefore
effectively mitigating the gradient vanishing problem persistent in other
recursive models. Both qualitative and quantitative analysis shows that AdaSent
can automatically form and select the representations suitable for the task at
hand during training, yielding superior classification performance over
competitor models on 5 benchmark data sets.Comment: 8 pages, 7 figures, accepted as a full paper at IJCAI 201
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