1,685 research outputs found
Deconvolutional Latent-Variable Model for Text Sequence Matching
A latent-variable model is introduced for text matching, inferring sentence
representations by jointly optimizing generative and discriminative objectives.
To alleviate typical optimization challenges in latent-variable models for
text, we employ deconvolutional networks as the sequence decoder (generator),
providing learned latent codes with more semantic information and better
generalization. Our model, trained in an unsupervised manner, yields stronger
empirical predictive performance than a decoder based on Long Short-Term Memory
(LSTM), with less parameters and considerably faster training. Further, we
apply it to text sequence-matching problems. The proposed model significantly
outperforms several strong sentence-encoding baselines, especially in the
semi-supervised setting.Comment: Accepted by AAAI-201
Unsupervised Controllable Text Formalization
We propose a novel framework for controllable natural language
transformation. Realizing that the requirement of parallel corpus is
practically unsustainable for controllable generation tasks, an unsupervised
training scheme is introduced. The crux of the framework is a deep neural
encoder-decoder that is reinforced with text-transformation knowledge through
auxiliary modules (called scorers). The scorers, based on off-the-shelf
language processing tools, decide the learning scheme of the encoder-decoder
based on its actions. We apply this framework for the text-transformation task
of formalizing an input text by improving its readability grade; the degree of
required formalization can be controlled by the user at run-time. Experiments
on public datasets demonstrate the efficacy of our model towards: (a)
transforming a given text to a more formal style, and (b) introducing
appropriate amount of formalness in the output text pertaining to the input
control. Our code and datasets are released for academic use.Comment: AAA
From Deterministic to Generative: Multi-Modal Stochastic RNNs for Video Captioning
Video captioning in essential is a complex natural process, which is affected
by various uncertainties stemming from video content, subjective judgment, etc.
In this paper we build on the recent progress in using encoder-decoder
framework for video captioning and address what we find to be a critical
deficiency of the existing methods, that most of the decoders propagate
deterministic hidden states. Such complex uncertainty cannot be modeled
efficiently by the deterministic models. In this paper, we propose a generative
approach, referred to as multi-modal stochastic RNNs networks (MS-RNN), which
models the uncertainty observed in the data using latent stochastic variables.
Therefore, MS-RNN can improve the performance of video captioning, and generate
multiple sentences to describe a video considering different random factors.
Specifically, a multi-modal LSTM (M-LSTM) is first proposed to interact with
both visual and textual features to capture a high-level representation. Then,
a backward stochastic LSTM (S-LSTM) is proposed to support uncertainty
propagation by introducing latent variables. Experimental results on the
challenging datasets MSVD and MSR-VTT show that our proposed MS-RNN approach
outperforms the state-of-the-art video captioning benchmarks
Hierarchical RNN with Static Sentence-Level Attention for Text-Based Speaker Change Detection
Speaker change detection (SCD) is an important task in dialog modeling. Our
paper addresses the problem of text-based SCD, which differs from existing
audio-based studies and is useful in various scenarios, for example, processing
dialog transcripts where speaker identities are missing (e.g., OpenSubtitle),
and enhancing audio SCD with textual information. We formulate text-based SCD
as a matching problem of utterances before and after a certain decision point;
we propose a hierarchical recurrent neural network (RNN) with static
sentence-level attention. Experimental results show that neural networks
consistently achieve better performance than feature-based approaches, and that
our attention-based model significantly outperforms non-attention neural
networks.Comment: In Proceedings of the ACM on Conference on Information and Knowledge
Management (CIKM), 201
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