42,257 research outputs found
Semantically Conditioned LSTM-based Natural Language Generation for Spoken Dialogue Systems
Natural language generation (NLG) is a critical component of spoken dialogue
and it has a significant impact both on usability and perceived quality. Most
NLG systems in common use employ rules and heuristics and tend to generate
rigid and stylised responses without the natural variation of human language.
They are also not easily scaled to systems covering multiple domains and
languages. This paper presents a statistical language generator based on a
semantically controlled Long Short-term Memory (LSTM) structure. The LSTM
generator can learn from unaligned data by jointly optimising sentence planning
and surface realisation using a simple cross entropy training criterion, and
language variation can be easily achieved by sampling from output candidates.
With fewer heuristics, an objective evaluation in two differing test domains
showed the proposed method improved performance compared to previous methods.
Human judges scored the LSTM system higher on informativeness and naturalness
and overall preferred it to the other systems.Comment: To be appear in EMNLP 201
GuessWhat?! Visual object discovery through multi-modal dialogue
We introduce GuessWhat?!, a two-player guessing game as a testbed for
research on the interplay of computer vision and dialogue systems. The goal of
the game is to locate an unknown object in a rich image scene by asking a
sequence of questions. Higher-level image understanding, like spatial reasoning
and language grounding, is required to solve the proposed task. Our key
contribution is the collection of a large-scale dataset consisting of 150K
human-played games with a total of 800K visual question-answer pairs on 66K
images. We explain our design decisions in collecting the dataset and introduce
the oracle and questioner tasks that are associated with the two players of the
game. We prototyped deep learning models to establish initial baselines of the
introduced tasks.Comment: 23 pages; CVPR 2017 submission; see https://guesswhat.a
Steps toward the power spectrum of matter. III. The primordial spectrum
Observed power spectrum of matter found in Papers I and II is compared with
analytical power spectra. Spatially flat cold and mixed dark matter models with
cosmological constant and open models are considered. The primordial power
spectrum of matter is determined using the power spectrum of matter and the
transfer functions of analytical models. The primordial power spectrum has a
break in amplitude. We conclude that a scale-free primordial power spectrum is
excluded if presently available data on the distribution of clusters and
galaxies represent the true mass distribution of the Universe.Comment: LaTex (sty files added), 22 pages, 5 PostScript figures embedded,
Astrophysical Journal (accepted
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