2,117 research outputs found
"The Boating Store Had Its Best Sail Ever": Pronunciation-attentive Contextualized Pun Recognition
Humor plays an important role in human languages and it is essential to model
humor when building intelligence systems. Among different forms of humor, puns
perform wordplay for humorous effects by employing words with double entendre
and high phonetic similarity. However, identifying and modeling puns are
challenging as puns usually involved implicit semantic or phonological tricks.
In this paper, we propose Pronunciation-attentive Contextualized Pun
Recognition (PCPR) to perceive human humor, detect if a sentence contains puns
and locate them in the sentence. PCPR derives contextualized representation for
each word in a sentence by capturing the association between the surrounding
context and its corresponding phonetic symbols. Extensive experiments are
conducted on two benchmark datasets. Results demonstrate that the proposed
approach significantly outperforms the state-of-the-art methods in pun
detection and location tasks. In-depth analyses verify the effectiveness and
robustness of PCPR.Comment: 10 pages, 4 figures, 7 tables, accepted by ACL 202
Improving Visually Grounded Sentence Representations with Self-Attention
Sentence representation models trained only on language could potentially
suffer from the grounding problem. Recent work has shown promising results in
improving the qualities of sentence representations by jointly training them
with associated image features. However, the grounding capability is limited
due to distant connection between input sentences and image features by the
design of the architecture. In order to further close the gap, we propose
applying self-attention mechanism to the sentence encoder to deepen the
grounding effect. Our results on transfer tasks show that self-attentive
encoders are better for visual grounding, as they exploit specific words with
strong visual associations
Debiasing Word Embeddings Improves Multimodal Machine Translation
In recent years, pretrained word embeddings have proved useful for multimodal
neural machine translation (NMT) models to address the shortage of available
datasets. However, the integration of pretrained word embeddings has not yet
been explored extensively. Further, pretrained word embeddings in high
dimensional spaces have been reported to suffer from the hubness problem.
Although some debiasing techniques have been proposed to address this problem
for other natural language processing tasks, they have seldom been studied for
multimodal NMT models. In this study, we examine various kinds of word
embeddings and introduce two debiasing techniques for three multimodal NMT
models and two language pairs -- English-German translation and English-French
translation. With our optimal settings, the overall performance of multimodal
models was improved by up to +1.93 BLEU and +2.02 METEOR for English-German
translation and +1.73 BLEU and +0.95 METEOR for English-French translation.Comment: 11 pages; MT Summit 2019 (camera ready
Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey
Text-based Question Answering (QA) is a challenging task which aims at
finding short concrete answers for users' questions. This line of research has
been widely studied with information retrieval techniques and has received
increasing attention in recent years by considering deep neural network
approaches. Deep learning approaches, which are the main focus of this paper,
provide a powerful technique to learn multiple layers of representations and
interaction between questions and texts. In this paper, we provide a
comprehensive overview of different models proposed for the QA task, including
both traditional information retrieval perspective, and more recent deep neural
network perspective. We also introduce well-known datasets for the task and
present available results from the literature to have a comparison between
different techniques
Visually Grounded Word Embeddings and Richer Visual Features for Improving Multimodal Neural Machine Translation
In Multimodal Neural Machine Translation (MNMT), a neural model generates a
translated sentence that describes an image, given the image itself and one
source descriptions in English. This is considered as the multimodal image
caption translation task. The images are processed with Convolutional Neural
Network (CNN) to extract visual features exploitable by the translation model.
So far, the CNNs used are pre-trained on object detection and localization
task. We hypothesize that richer architecture, such as dense captioning models,
may be more suitable for MNMT and could lead to improved translations. We
extend this intuition to the word-embeddings, where we compute both linguistic
and visual representation for our corpus vocabulary. We combine and compare
different confiComment: Accepted to GLU 2017. arXiv admin note: text overlap with
arXiv:1707.0099
Dialogue Act Classification with Context-Aware Self-Attention
Recent work in Dialogue Act classification has treated the task as a sequence
labeling problem using hierarchical deep neural networks. We build on this
prior work by leveraging the effectiveness of a context-aware self-attention
mechanism coupled with a hierarchical recurrent neural network. We conduct
extensive evaluations on standard Dialogue Act classification datasets and show
significant improvement over state-of-the-art results on the Switchboard
Dialogue Act (SwDA) Corpus. We also investigate the impact of different
utterance-level representation learning methods and show that our method is
effective at capturing utterance-level semantic text representations while
maintaining high accuracy.Comment: NAACL-HLT 2019. 7 pages, 3 figure
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks
Learning distributed sentence representations is one of the key challenges in
natural language processing. Previous work demonstrated that a recurrent neural
network (RNNs) based sentence encoder trained on a large collection of
annotated natural language inference data, is efficient in the transfer
learning to facilitate other related tasks. In this paper, we show that joint
learning of multiple tasks results in better generalizable sentence
representations by conducting extensive experiments and analysis comparing the
multi-task and single-task learned sentence encoders. The quantitative analysis
using auxiliary tasks show that multi-task learning helps to embed better
semantic information in the sentence representations compared to single-task
learning. In addition, we compare multi-task sentence encoders with
contextualized word representations and show that combining both of them can
further boost the performance of transfer learning
Reasoning with Sarcasm by Reading In-between
Sarcasm is a sophisticated speech act which commonly manifests on social
communities such as Twitter and Reddit. The prevalence of sarcasm on the social
web is highly disruptive to opinion mining systems due to not only its tendency
of polarity flipping but also usage of figurative language. Sarcasm commonly
manifests with a contrastive theme either between positive-negative sentiments
or between literal-figurative scenarios. In this paper, we revisit the notion
of modeling contrast in order to reason with sarcasm. More specifically, we
propose an attention-based neural model that looks in-between instead of
across, enabling it to explicitly model contrast and incongruity. We conduct
extensive experiments on six benchmark datasets from Twitter, Reddit and the
Internet Argument Corpus. Our proposed model not only achieves state-of-the-art
performance on all datasets but also enjoys improved interpretability.Comment: Accepted to ACL201
Abstractive Summarization Using Attentive Neural Techniques
In a world of proliferating data, the ability to rapidly summarize text is
growing in importance. Automatic summarization of text can be thought of as a
sequence to sequence problem. Another area of natural language processing that
solves a sequence to sequence problem is machine translation, which is rapidly
evolving due to the development of attention-based encoder-decoder networks.
This work applies these modern techniques to abstractive summarization. We
perform analysis on various attention mechanisms for summarization with the
goal of developing an approach and architecture aimed at improving the state of
the art. In particular, we modify and optimize a translation model with
self-attention for generating abstractive sentence summaries. The effectiveness
of this base model along with attention variants is compared and analyzed in
the context of standardized evaluation sets and test metrics. However, we show
that these metrics are limited in their ability to effectively score
abstractive summaries, and propose a new approach based on the intuition that
an abstractive model requires an abstractive evaluation.Comment: Accepted for oral presentation at the 15th International Conference
on Natural Language Processing (ICON 2018
Multi-labeled Relation Extraction with Attentive Capsule Network
To disclose overlapped multiple relations from a sentence still keeps
challenging. Most current works in terms of neural models inconveniently
assuming that each sentence is explicitly mapped to a relation label, cannot
handle multiple relations properly as the overlapped features of the relations
are either ignored or very difficult to identify. To tackle with the new issue,
we propose a novel approach for multi-labeled relation extraction with capsule
network which acts considerably better than current convolutional or recurrent
net in identifying the highly overlapped relations within an individual
sentence. To better cluster the features and precisely extract the relations,
we further devise attention-based routing algorithm and sliding-margin loss
function, and embed them into our capsule network. The experimental results
show that the proposed approach can indeed extract the highly overlapped
features and achieve significant performance improvement for relation
extraction comparing to the state-of-the-art works.Comment: To be published in AAAI 201
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