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1,500 | DeepStory: Video Story QA by Deep Embedded Memory Networks | cs.CV | Question-answering (QA) on video contents is a significant challenge for
achieving human-level intelligence as it involves both vision and language in
real-world settings. Here we demonstrate the possibility of an AI agent
performing video story QA by learning from a large amount of cartoon videos. We
develop a video-s... | computer science |
1,501 | Cross-linguistic differences and similarities in image descriptions | cs.CL | Automatic image description systems are commonly trained and evaluated on
large image description datasets. Recently, researchers have started to collect
such datasets for languages other than English. An unexplored question is how
different these datasets are from English and, if there are any differences,
what causes... | computer science |
1,502 | Evaluating Visual Conversational Agents via Cooperative Human-AI Games | cs.HC | As AI continues to advance, human-AI teams are inevitable. However, progress
in AI is routinely measured in isolation, without a human in the loop. It is
crucial to benchmark progress in AI, not just in isolation, but also in terms
of how it translates to helping humans perform certain tasks, i.e., the
performance of h... | computer science |
1,503 | Whodunnit? Crime Drama as a Case for Natural Language Understanding | cs.CL | In this paper we argue that crime drama exemplified in television programs
such as CSI:Crime Scene Investigation is an ideal testbed for approximating
real-world natural language understanding and the complex inferences associated
with it. We propose to treat crime drama as a new inference task, capitalizing
on the fac... | computer science |
1,504 | Co-attending Free-form Regions and Detections with Multi-modal
Multiplicative Feature Embedding for Visual Question Answering | cs.CV | Recently, the Visual Question Answering (VQA) task has gained increasing
attention in artificial intelligence. Existing VQA methods mainly adopt the
visual attention mechanism to associate the input question with corresponding
image regions for effective question answering. The free-form region based and
the detection-... | computer science |
1,505 | Vision-and-Language Navigation: Interpreting visually-grounded
navigation instructions in real environments | cs.CV | A robot that can carry out a natural-language instruction has been a dream
since before the Jetsons cartoon series imagined a life of leisure mediated by
a fleet of attentive robot helpers. It is a dream that remains stubbornly
distant. However, recent advances in vision and language methods have made
incredible progre... | computer science |
1,506 | Are You Talking to Me? Reasoned Visual Dialog Generation through
Adversarial Learning | cs.CV | The Visual Dialogue task requires an agent to engage in a conversation about
an image with a human. It represents an extension of the Visual Question
Answering task in that the agent needs to answer a question about an image, but
it needs to do so in light of the previous dialogue that has taken place. The
key challeng... | computer science |
1,507 | Multimodal Storytelling via Generative Adversarial Imitation Learning | cs.AI | Deriving event storylines is an effective summarization method to succinctly
organize extensive information, which can significantly alleviate the pain of
information overload. The critical challenge is the lack of widely recognized
definition of storyline metric. Prior studies have developed various approaches
based o... | computer science |
1,508 | Interpretable Counting for Visual Question Answering | cs.AI | Questions that require counting a variety of objects in images remain a major
challenge in visual question answering (VQA). The most common approaches to VQA
involve either classifying answers based on fixed length representations of
both the image and question or summing fractional counts estimated from each
section o... | computer science |
1,509 | Efficient Large-Scale Multi-Modal Classification | cs.CL | While the incipient internet was largely text-based, the modern digital world
is becoming increasingly multi-modal. Here, we examine multi-modal
classification where one modality is discrete, e.g. text, and the other is
continuous, e.g. visual representations transferred from a convolutional neural
network. In particul... | computer science |
1,510 | Multimodal Explanations: Justifying Decisions and Pointing to the
Evidence | cs.AI | Deep models that are both effective and explainable are desirable in many
settings; prior explainable models have been unimodal, offering either
image-based visualization of attention weights or text-based generation of
post-hoc justifications. We propose a multimodal approach to explanation, and
argue that the two mod... | computer science |
1,511 | Face2Text: Collecting an Annotated Image Description Corpus for the
Generation of Rich Face Descriptions | cs.CL | The past few years have witnessed renewed interest in NLP tasks at the
interface between vision and language. One intensively-studied problem is that
of automatically generating text from images. In this paper, we extend this
problem to the more specific domain of face description. Unlike scene
descriptions, face descr... | computer science |
1,512 | Attention on Attention: Architectures for Visual Question Answering
(VQA) | cs.CL | Visual Question Answering (VQA) is an increasingly popular topic in deep
learning research, requiring coordination of natural language processing and
computer vision modules into a single architecture. We build upon the model
which placed first in the VQA Challenge by developing thirteen new attention
mechanisms and in... | computer science |
1,513 | Look Before You Leap: Bridging Model-Free and Model-Based Reinforcement
Learning for Planned-Ahead Vision-and-Language Navigation | cs.CV | Existing research studies on vision and language grounding for robot
navigation focus on improving model-free deep reinforcement learning (DRL)
models in synthetic environments. However, model-free DRL models do not
consider the dynamics in the real-world environments, and they often fail to
generalize to new scenes. I... | computer science |
1,514 | Seeing What You're Told: Sentence-Guided Activity Recognition In Video | cs.CV | We present a system that demonstrates how the compositional structure of
events, in concert with the compositional structure of language, can interplay
with the underlying focusing mechanisms in video action recognition, thereby
providing a medium, not only for top-down and bottom-up integration, but also
for multi-mod... | computer science |
1,515 | Visual Storytelling | cs.CL | We introduce the first dataset for sequential vision-to-language, and explore
how this data may be used for the task of visual storytelling. The first
release of this dataset, SIND v.1, includes 81,743 unique photos in 20,211
sequences, aligned to both descriptive (caption) and story language. We
establish several stro... | computer science |
1,516 | An Approach to the Analysis of the South Slavic Medieval Labels Using
Image Texture | cs.CV | The paper presents a new script classification method for the discrimination
of the South Slavic medieval labels. It consists in the textural analysis of
the script types. In the first step, each letter is coded by the equivalent
script type, which is defined by its typographical features. Obtained coded
text is subjec... | computer science |
1,517 | Deep Multimodal Semantic Embeddings for Speech and Images | cs.CV | In this paper, we present a model which takes as input a corpus of images
with relevant spoken captions and finds a correspondence between the two
modalities. We employ a pair of convolutional neural networks to model visual
objects and speech signals at the word level, and tie the networks together
with an embedding a... | computer science |
1,518 | Virtual Embodiment: A Scalable Long-Term Strategy for Artificial
Intelligence Research | cs.AI | Meaning has been called the "holy grail" of a variety of scientific
disciplines, ranging from linguistics to philosophy, psychology and the
neurosciences. The field of Artifical Intelligence (AI) is very much a part of
that list: the development of sophisticated natural language semantics is a
sine qua non for achievin... | computer science |
1,519 | Zero-resource Machine Translation by Multimodal Encoder-decoder Network
with Multimedia Pivot | cs.CL | We propose an approach to build a neural machine translation system with no
supervised resources (i.e., no parallel corpora) using multimodal embedded
representation over texts and images. Based on the assumption that text
documents are often likely to be described with other multimedia information
(e.g., images) somew... | computer science |
1,520 | Natural Language Does Not Emerge 'Naturally' in Multi-Agent Dialog | cs.CL | A number of recent works have proposed techniques for end-to-end learning of
communication protocols among cooperative multi-agent populations, and have
simultaneously found the emergence of grounded human-interpretable language in
the protocols developed by the agents, all learned without any human
supervision!
In t... | computer science |
1,521 | Video Question Answering via Attribute-Augmented Attention Network
Learning | cs.CV | Video Question Answering is a challenging problem in visual information
retrieval, which provides the answer to the referenced video content according
to the question. However, the existing visual question answering approaches
mainly tackle the problem of static image question, which may be ineffectively
for video ques... | computer science |
1,522 | Learning Multi-Modal Word Representation Grounded in Visual Context | cs.CL | Representing the semantics of words is a long-standing problem for the
natural language processing community. Most methods compute word semantics
given their textual context in large corpora. More recently, researchers
attempted to integrate perceptual and visual features. Most of these works
consider the visual appear... | computer science |
1,523 | Phrase-based Image Captioning with Hierarchical LSTM Model | cs.CV | Automatic generation of caption to describe the content of an image has been
gaining a lot of research interests recently, where most of the existing works
treat the image caption as pure sequential data. Natural language, however
possess a temporal hierarchy structure, with complex dependencies between each
subsequenc... | computer science |
1,524 | Asking the Difficult Questions: Goal-Oriented Visual Question Generation
via Intermediate Rewards | cs.CV | Despite significant progress in a variety of vision-and-language problems,
developing a method capable of asking intelligent, goal-oriented questions
about images is proven to be an inscrutable challenge. Towards this end, we
propose a Deep Reinforcement Learning framework based on three new intermediate
rewards, namel... | computer science |
1,525 | Video Captioning via Hierarchical Reinforcement Learning | cs.CV | Video captioning is the task of automatically generating a textual
description of the actions in a video. Although previous work (e.g.
sequence-to-sequence model) has shown promising results in abstracting a coarse
description of a short video, it is still very challenging to caption a video
containing multiple fine-gr... | computer science |
1,526 | MAttNet: Modular Attention Network for Referring Expression
Comprehension | cs.CV | In this paper, we address referring expression comprehension: localizing an
image region described by a natural language expression. While most recent work
treats expressions as a single unit, we propose to decompose them into three
modular components related to subject appearance, location, and relationship to
other o... | computer science |
1,527 | A Deep Learning Approach for Multimodal Deception Detection | cs.CL | Automatic deception detection is an important task that has gained momentum
in computational linguistics due to its potential applications. In this paper,
we propose a simple yet tough to beat multi-modal neural model for deception
detection. By combining features from different modalities such as video,
audio, and tex... | computer science |
1,528 | Oracle performance for visual captioning | cs.CV | The task of associating images and videos with a natural language description
has attracted a great amount of attention recently. Rapid progress has been
made in terms of both developing novel algorithms and releasing new datasets.
Indeed, the state-of-the-art results on some of the standard datasets have been
pushed i... | computer science |
1,529 | Attention networks for image-to-text | cs.CV | The paper approaches the problem of image-to-text with attention-based
encoder-decoder networks that are trained to handle sequences of characters
rather than words. We experiment on lines of text from a popular handwriting
database with different attention mechanisms for the decoder. The model trained
with softmax att... | computer science |
1,530 | The Modular Audio Recognition Framework (MARF) and its Applications:
Scientific and Software Engineering Notes | cs.SD | MARF is an open-source research platform and a collection of
voice/sound/speech/text and natural language processing (NLP) algorithms
written in Java and arranged into a modular and extensible framework
facilitating addition of new algorithms. MARF can run distributively over the
network and may act as a library in app... | computer science |
1,531 | Inducing a Semantically Annotated Lexicon via EM-Based Clustering | cs.CL | We present a technique for automatic induction of slot annotations for
subcategorization frames, based on induction of hidden classes in the EM
framework of statistical estimation. The models are empirically evalutated by a
general decision test. Induction of slot labeling for subcategorization frames
is accomplished b... | computer science |
1,532 | A Classification Approach to Word Prediction | cs.CL | The eventual goal of a language model is to accurately predict the value of a
missing word given its context. We present an approach to word prediction that
is based on learning a representation for each word as a function of words and
linguistics predicates in its context. This approach raises a few new questions
that... | computer science |
1,533 | A Sequential Model for Multi-Class Classification | cs.AI | Many classification problems require decisions among a large number of
competing classes. These tasks, however, are not handled well by general
purpose learning methods and are usually addressed in an ad-hoc fashion. We
suggest a general approach -- a sequential learning model that utilizes
classifiers to sequentially ... | computer science |
1,534 | Large-Margin Learning of Submodular Summarization Methods | cs.AI | In this paper, we present a supervised learning approach to training
submodular scoring functions for extractive multi-document summarization. By
taking a structured predicition approach, we provide a large-margin method that
directly optimizes a convex relaxation of the desired performance measure. The
learning method... | computer science |
1,535 | Domain and Function: A Dual-Space Model of Semantic Relations and
Compositions | cs.CL | Given appropriate representations of the semantic relations between carpenter
and wood and between mason and stone (for example, vectors in a vector space
model), a suitable algorithm should be able to recognize that these relations
are highly similar (carpenter is to wood as mason is to stone; the relations
are analog... | computer science |
1,536 | Embedding Lexical Features via Low-Rank Tensors | cs.CL | Modern NLP models rely heavily on engineered features, which often combine
word and contextual information into complex lexical features. Such combination
results in large numbers of features, which can lead to over-fitting. We
present a new model that represents complex lexical features---comprised of
parts for words,... | computer science |
1,537 | Understanding Rating Behaviour and Predicting Ratings by Identifying
Representative Users | cs.IR | Online user reviews describing various products and services are now abundant
on the web. While the information conveyed through review texts and ratings is
easily comprehensible, there is a wealth of hidden information in them that is
not immediately obvious. In this study, we unlock this hidden value behind user
revi... | computer science |
1,538 | Learning to Win by Reading Manuals in a Monte-Carlo Framework | cs.CL | Domain knowledge is crucial for effective performance in autonomous control
systems. Typically, human effort is required to encode this knowledge into a
control algorithm. In this paper, we present an approach to language grounding
which automatically interprets text in the context of a complex control
application, suc... | computer science |
1,539 | Experiments with Three Approaches to Recognizing Lexical Entailment | cs.CL | Inference in natural language often involves recognizing lexical entailment
(RLE); that is, identifying whether one word entails another. For example,
"buy" entails "own". Two general strategies for RLE have been proposed: One
strategy is to manually construct an asymmetric similarity measure for context
vectors (direc... | computer science |
1,540 | Compositional Distributional Semantics with Long Short Term Memory | cs.CL | We are proposing an extension of the recursive neural network that makes use
of a variant of the long short-term memory architecture. The extension allows
information low in parse trees to be stored in a memory register (the `memory
cell') and used much later higher up in the parse tree. This provides a
solution to the... | computer science |
1,541 | Interpretable Semantic Textual Similarity: Finding and explaining
differences between sentences | cs.CL | User acceptance of artificial intelligence agents might depend on their
ability to explain their reasoning, which requires adding an interpretability
layer that fa- cilitates users to understand their behavior. This paper focuses
on adding an in- terpretable layer on top of Semantic Textual Similarity (STS),
which meas... | computer science |
1,542 | A User Simulator for Task-Completion Dialogues | cs.LG | Despite widespread interests in reinforcement-learning for task-oriented
dialogue systems, several obstacles can frustrate research and development
progress. First, reinforcement learners typically require interaction with the
environment, so conventional dialogue corpora cannot be used directly. Second,
each task pres... | computer science |
1,543 | Durkheim Project Data Analysis Report | cs.AI | This report describes the suicidality prediction models created under the
DARPA DCAPS program in association with the Durkheim Project
[http://durkheimproject.org/]. The models were built primarily from
unstructured text (free-format clinician notes) for several hundred patient
records obtained from the Veterans Health... | computer science |
1,544 | Combining Two And Three-Way Embeddings Models for Link Prediction in
Knowledge Bases | cs.AI | This paper tackles the problem of endogenous link prediction for Knowledge
Base completion. Knowledge Bases can be represented as directed graphs whose
nodes correspond to entities and edges to relationships. Previous attempts
either consist of powerful systems with high capacity to model complex
connectivity patterns,... | computer science |
1,545 | Document Embedding with Paragraph Vectors | cs.CL | Paragraph Vectors has been recently proposed as an unsupervised method for
learning distributed representations for pieces of texts. In their work, the
authors showed that the method can learn an embedding of movie review texts
which can be leveraged for sentiment analysis. That proof of concept, while
encouraging, was... | computer science |
1,546 | Review-Level Sentiment Classification with Sentence-Level Polarity
Correction | cs.CL | We propose an effective technique to solving review-level sentiment
classification problem by using sentence-level polarity correction. Our
polarity correction technique takes into account the consistency of the
polarities (positive and negative) of sentences within each product review
before performing the actual mach... | computer science |
1,547 | Science Question Answering using Instructional Materials | cs.CL | We provide a solution for elementary science test using instructional
materials. We posit that there is a hidden structure that explains the
correctness of an answer given the question and instructional materials and
present a unified max-margin framework that learns to find these hidden
structures (given a corpus of q... | computer science |
1,548 | Robust Dialog State Tracking for Large Ontologies | cs.CL | The Dialog State Tracking Challenge 4 (DSTC 4) differentiates itself from the
previous three editions as follows: the number of slot-value pairs present in
the ontology is much larger, no spoken language understanding output is given,
and utterances are labeled at the subdialog level. This paper describes a novel
dialo... | computer science |
1,549 | End-to-end LSTM-based dialog control optimized with supervised and
reinforcement learning | cs.CL | This paper presents a model for end-to-end learning of task-oriented dialog
systems. The main component of the model is a recurrent neural network (an
LSTM), which maps from raw dialog history directly to a distribution over
system actions. The LSTM automatically infers a representation of dialog
history, which relieve... | computer science |
1,550 | "Show me the cup": Reference with Continuous Representations | cs.CL | One of the most basic functions of language is to refer to objects in a
shared scene. Modeling reference with continuous representations is challenging
because it requires individuation, i.e., tracking and distinguishing an
arbitrary number of referents. We introduce a neural network model that, given
a definite descri... | computer science |
1,551 | Domain Adaptation for Neural Networks by Parameter Augmentation | cs.CL | We propose a simple domain adaptation method for neural networks in a
supervised setting. Supervised domain adaptation is a way of improving the
generalization performance on the target domain by using the source domain
dataset, assuming that both of the datasets are labeled. Recently, recurrent
neural networks have be... | computer science |
1,552 | Bi-directional Attention with Agreement for Dependency Parsing | cs.CL | We develop a novel bi-directional attention model for dependency parsing,
which learns to agree on headword predictions from the forward and backward
parsing directions. The parsing procedure for each direction is formulated as
sequentially querying the memory component that stores continuous headword
embeddings. The p... | computer science |
1,553 | Google's Neural Machine Translation System: Bridging the Gap between
Human and Machine Translation | cs.CL | Neural Machine Translation (NMT) is an end-to-end learning approach for
automated translation, with the potential to overcome many of the weaknesses of
conventional phrase-based translation systems. Unfortunately, NMT systems are
known to be computationally expensive both in training and in translation
inference. Also,... | computer science |
1,554 | Navigational Instruction Generation as Inverse Reinforcement Learning
with Neural Machine Translation | cs.RO | Modern robotics applications that involve human-robot interaction require
robots to be able to communicate with humans seamlessly and effectively.
Natural language provides a flexible and efficient medium through which robots
can exchange information with their human partners. Significant advancements
have been made in... | computer science |
1,555 | Low-rank and Sparse Soft Targets to Learn Better DNN Acoustic Models | cs.CL | Conventional deep neural networks (DNN) for speech acoustic modeling rely on
Gaussian mixture models (GMM) and hidden Markov model (HMM) to obtain binary
class labels as the targets for DNN training. Subword classes in speech
recognition systems correspond to context-dependent tied states or senones. The
present work a... | computer science |
1,556 | Learning to Play Guess Who? and Inventing a Grounded Language as a
Consequence | cs.AI | Acquiring your first language is an incredible feat and not easily
duplicated. Learning to communicate using nothing but a few pictureless books,
a corpus, would likely be impossible even for humans. Nevertheless, this is the
dominating approach in most natural language processing today. As an
alternative, we propose t... | computer science |
1,557 | What the Language You Tweet Says About Your Occupation | cs.CY | Many aspects of people's lives are proven to be deeply connected to their
jobs. In this paper, we first investigate the distinct characteristics of major
occupation categories based on tweets. From multiple social media platforms, we
gather several types of user information. From users' LinkedIn webpages, we
learn thei... | computer science |
1,558 | Rationalization: A Neural Machine Translation Approach to Generating
Natural Language Explanations | cs.AI | We introduce AI rationalization, an approach for generating explanations of
autonomous system behavior as if a human had performed the behavior. We
describe a rationalization technique that uses neural machine translation to
translate internal state-action representations of an autonomous agent into
natural language. W... | computer science |
1,559 | RACE: Large-scale ReAding Comprehension Dataset From Examinations | cs.CL | We present RACE, a new dataset for benchmark evaluation of methods in the
reading comprehension task. Collected from the English exams for middle and
high school Chinese students in the age range between 12 to 18, RACE consists
of near 28,000 passages and near 100,000 questions generated by human experts
(English instr... | computer science |
1,560 | Past, Present, Future: A Computational Investigation of the Typology of
Tense in 1000 Languages | cs.CL | We present SuperPivot, an analysis method for low-resource languages that
occur in a superparallel corpus, i.e., in a corpus that contains an order of
magnitude more languages than parallel corpora currently in use. We show that
SuperPivot performs well for the crosslingual analysis of the linguistic
phenomenon of tens... | computer science |
1,561 | Sequential Dialogue Context Modeling for Spoken Language Understanding | cs.CL | Spoken Language Understanding (SLU) is a key component of goal oriented
dialogue systems that would parse user utterances into semantic frame
representations. Traditionally SLU does not utilize the dialogue history beyond
the previous system turn and contextual ambiguities are resolved by the
downstream components. In ... | computer science |
1,562 | Controllable Invariance through Adversarial Feature Learning | cs.LG | Learning meaningful representations that maintain the content necessary for a
particular task while filtering away detrimental variations is a problem of
great interest in machine learning. In this paper, we tackle the problem of
learning representations invariant to a specific factor or trait of data. The
representati... | computer science |
1,563 | Semantic Specialisation of Distributional Word Vector Spaces using
Monolingual and Cross-Lingual Constraints | cs.CL | We present Attract-Repel, an algorithm for improving the semantic quality of
word vectors by injecting constraints extracted from lexical resources.
Attract-Repel facilitates the use of constraints from mono- and cross-lingual
resources, yielding semantically specialised cross-lingual vector spaces. Our
evaluation show... | computer science |
1,564 | Sample-efficient Actor-Critic Reinforcement Learning with Supervised
Data for Dialogue Management | cs.CL | Deep reinforcement learning (RL) methods have significant potential for
dialogue policy optimisation. However, they suffer from a poor performance in
the early stages of learning. This is especially problematic for on-line
learning with real users. Two approaches are introduced to tackle this problem.
Firstly, to speed... | computer science |
1,565 | PELESent: Cross-domain polarity classification using distant supervision | cs.CL | The enormous amount of texts published daily by Internet users has fostered
the development of methods to analyze this content in several natural language
processing areas, such as sentiment analysis. The main goal of this task is to
classify the polarity of a message. Even though many approaches have been
proposed for... | computer science |
1,566 | Source-Target Inference Models for Spatial Instruction Understanding | cs.CL | Models that can execute natural language instructions for situated robotic
tasks such as assembly and navigation have several useful applications in
homes, offices, and remote scenarios. We study the semantics of
spatially-referred configuration and arrangement instructions, based on the
challenging Bisk-2016 blank-lab... | computer science |
1,567 | Reinforcement Learning for Bandit Neural Machine Translation with
Simulated Human Feedback | cs.CL | Machine translation is a natural candidate problem for reinforcement learning
from human feedback: users provide quick, dirty ratings on candidate
translations to guide a system to improve. Yet, current neural machine
translation training focuses on expensive human-generated reference
translations. We describe a reinfo... | computer science |
1,568 | Share your Model instead of your Data: Privacy Preserving Mimic Learning
for Ranking | cs.IR | Deep neural networks have become a primary tool for solving problems in many
fields. They are also used for addressing information retrieval problems and
show strong performance in several tasks. Training these models requires large,
representative datasets and for most IR tasks, such data contains sensitive
informatio... | computer science |
1,569 | Order-Planning Neural Text Generation From Structured Data | cs.CL | Generating texts from structured data (e.g., a table) is important for
various natural language processing tasks such as question answering and dialog
systems. In recent studies, researchers use neural language models and
encoder-decoder frameworks for table-to-text generation. However, these neural
network-based appro... | computer science |
1,570 | Neural Network Based Nonlinear Weighted Finite Automata | cs.FL | Weighted finite automata (WFA) can expressively model functions defined over
strings but are inherently linear models. Given the recent successes of
nonlinear models in machine learning, it is natural to wonder whether
ex-tending WFA to the nonlinear setting would be beneficial. In this paper, we
propose a novel model ... | computer science |
1,571 | Learning how to learn: an adaptive dialogue agent for incrementally
learning visually grounded word meanings | cs.CL | We present an optimised multi-modal dialogue agent for interactive learning
of visually grounded word meanings from a human tutor, trained on real
human-human tutoring data. Within a life-long interactive learning period, the
agent, trained using Reinforcement Learning (RL), must be able to handle
natural conversations... | computer science |
1,572 | Generalization without systematicity: On the compositional skills of
sequence-to-sequence recurrent networks | cs.CL | Humans can understand and produce new utterances effortlessly, thanks to
their compositional skills. Once a person learns the meaning of a new verb
"dax," he or she can immediately understand the meaning of "dax twice" or "sing
and dax." In this paper, we introduce the SCAN domain, consisting of a set of
simple composi... | computer science |
1,573 | Weakly-supervised Semantic Parsing with Abstract Examples | cs.CL | Semantic parsers translate language utterances to programs, but are often
trained from utterance-denotation pairs only. Consequently, parsers must
overcome the problem of spuriousness at training time, where an incorrect
program found at search time accidentally leads to a correct denotation. We
propose that in small w... | computer science |
1,574 | BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for
Task-Oriented Dialogue Systems | cs.AI | We present a new algorithm that significantly improves the efficiency of
exploration for deep Q-learning agents in dialogue systems. Our agents explore
via Thompson sampling, drawing Monte Carlo samples from a Bayes-by-Backprop
neural network. Our algorithm learns much faster than common exploration
strategies such as ... | computer science |
1,575 | Multi-attention Recurrent Network for Human Communication Comprehension | cs.AI | Human face-to-face communication is a complex multimodal signal. We use words
(language modality), gestures (vision modality) and changes in tone (acoustic
modality) to convey our intentions. Humans easily process and understand
face-to-face communication, however, comprehending this form of communication
remains a sig... | computer science |
1,576 | Interactive Grounded Language Acquisition and Generalization in a 2D
World | cs.CL | We build a virtual agent for learning language in a 2D maze-like world. The
agent sees images of the surrounding environment, listens to a virtual teacher,
and takes actions to receive rewards. It interactively learns the teacher's
language from scratch based on two language use cases: sentence-directed
navigation and ... | computer science |
1,577 | Decoding-History-Based Adaptive Control of Attention for Neural Machine
Translation | cs.CL | Attention-based sequence-to-sequence model has proved successful in Neural
Machine Translation (NMT). However, the attention without consideration of
decoding history, which includes the past information in the decoder and the
attention mechanism, often causes much repetition. To address this problem, we
propose the de... | computer science |
1,578 | ReinforceWalk: Learning to Walk in Graph with Monte Carlo Tree Search | cs.AI | Learning to walk over a graph towards a target node for a given input query
and a source node is an important problem in applications such as knowledge
graph reasoning. It can be formulated as a reinforcement learning (RL) problem
that has a known state transition model, but with partial observability and
sparse reward... | computer science |
1,579 | An Empirical Evaluation of Generic Convolutional and Recurrent Networks
for Sequence Modeling | cs.LG | For most deep learning practitioners, sequence modeling is synonymous with
recurrent networks. Yet recent results indicate that convolutional
architectures can outperform recurrent networks on tasks such as audio
synthesis and machine translation. Given a new sequence modeling task or
dataset, which architecture should... | computer science |
1,580 | Similarity-Based Models of Word Cooccurrence Probabilities | cs.CL | In many applications of natural language processing (NLP) it is necessary to
determine the likelihood of a given word combination. For example, a speech
recognizer may need to determine which of the two word combinations ``eat a
peach'' and ``eat a beach'' is more likely. Statistical NLP methods determine
the likelihoo... | computer science |
1,581 | Acquiring Lexical Paraphrases from a Single Corpus | cs.CL | This paper studies the potential of identifying lexical paraphrases within a
single corpus, focusing on the extraction of verb paraphrases. Most previous
approaches detect individual paraphrase instances within a pair (or set) of
comparable corpora, each of them containing roughly the same information, and
rely on the ... | computer science |
1,582 | Expressing Implicit Semantic Relations without Supervision | cs.CL | We present an unsupervised learning algorithm that mines large text corpora
for patterns that express implicit semantic relations. For a given input word
pair X:Y with some unspecified semantic relations, the corresponding output
list of patterns <P1,...,Pm> is ranked according to how well each pattern Pi
expresses the... | computer science |
1,583 | Using Soft Constraints To Learn Semantic Models Of Descriptions Of
Shapes | cs.CL | The contribution of this paper is to provide a semantic model (using soft
constraints) of the words used by web-users to describe objects in a language
game; a game in which one user describes a selected object of those composing
the scene, and another user has to guess which object has been described. The
given descri... | computer science |
1,584 | Machine Learning, Clustering, and Polymorphy | cs.AI | This paper describes a machine induction program (WITT) that attempts to
model human categorization. Properties of categories to which human subjects
are sensitive includes best or prototypical members, relative contrasts between
putative categories, and polymorphy (neither necessary or sufficient features).
This appro... | computer science |
1,585 | The structure of verbal sequences analyzed with unsupervised learning
techniques | cs.CL | Data mining allows the exploration of sequences of phenomena, whereas one
usually tends to focus on isolated phenomena or on the relation between two
phenomena. It offers invaluable tools for theoretical analyses and exploration
of the structure of sentences, texts, dialogues, and speech. We report here the
results of ... | computer science |
1,586 | Analogy perception applied to seven tests of word comprehension | cs.AI | It has been argued that analogy is the core of cognition. In AI research,
algorithms for analogy are often limited by the need for hand-coded high-level
representations as input. An alternative approach is to use high-level
perception, in which high-level representations are automatically generated
from raw data. Analo... | computer science |
1,587 | The Latent Relation Mapping Engine: Algorithm and Experiments | cs.CL | Many AI researchers and cognitive scientists have argued that analogy is the
core of cognition. The most influential work on computational modeling of
analogy-making is Structure Mapping Theory (SMT) and its implementation in the
Structure Mapping Engine (SME). A limitation of SME is the requirement for
complex hand-co... | computer science |
1,588 | FrameNet CNL: a Knowledge Representation and Information Extraction
Language | cs.CL | The paper presents a FrameNet-based information extraction and knowledge
representation framework, called FrameNet-CNL. The framework is used on natural
language documents and represents the extracted knowledge in a tailor-made
Frame-ontology from which unambiguous FrameNet-CNL paraphrase text can be
generated automati... | computer science |
1,589 | The OS* Algorithm: a Joint Approach to Exact Optimization and Sampling | cs.AI | Most current sampling algorithms for high-dimensional distributions are based
on MCMC techniques and are approximate in the sense that they are valid only
asymptotically. Rejection sampling, on the other hand, produces valid samples,
but is unrealistically slow in high-dimension spaces. The OS* algorithm that we
propos... | computer science |
1,590 | Grounded Discovery of Coordinate Term Relationships between Software
Entities | cs.CL | We present an approach for the detection of coordinate-term relationships
between entities from the software domain, that refer to Java classes. Usually,
relations are found by examining corpus statistics associated with text
entities. In some technical domains, however, we have access to additional
information about t... | computer science |
1,591 | Improved Relation Extraction with Feature-Rich Compositional Embedding
Models | cs.CL | Compositional embedding models build a representation (or embedding) for a
linguistic structure based on its component word embeddings. We propose a
Feature-rich Compositional Embedding Model (FCM) for relation extraction that
is expressive, generalizes to new domains, and is easy-to-implement. The key
idea is to combi... | computer science |
1,592 | Character-Level Question Answering with Attention | cs.CL | We show that a character-level encoder-decoder framework can be successfully
applied to question answering with a structured knowledge base. We use our
model for single-relation question answering and demonstrate the effectiveness
of our approach on the SimpleQuestions dataset (Bordes et al., 2015), where we
improve st... | computer science |
1,593 | Distributed Representations of Sentences and Documents | cs.CL | Many machine learning algorithms require the input to be represented as a
fixed-length feature vector. When it comes to texts, one of the most common
fixed-length features is bag-of-words. Despite their popularity, bag-of-words
features have two major weaknesses: they lose the ordering of the words and
they also ignore... | computer science |
1,594 | Semantic Composition and Decomposition: From Recognition to Generation | cs.CL | Semantic composition is the task of understanding the meaning of text by
composing the meanings of the individual words in the text. Semantic
decomposition is the task of understanding the meaning of an individual word by
decomposing it into various aspects (factors, constituents, components) that
are latent in the mea... | computer science |
1,595 | Improved Semantic Representations From Tree-Structured Long Short-Term
Memory Networks | cs.CL | Because of their superior ability to preserve sequence information over time,
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with
a more complex computational unit, have obtained strong results on a variety of
sequence modeling tasks. The only underlying LSTM structure that has been
explored... | computer science |
1,596 | Robustly Leveraging Prior Knowledge in Text Classification | cs.CL | Prior knowledge has been shown very useful to address many natural language
processing tasks. Many approaches have been proposed to formalise a variety of
knowledge, however, whether the proposed approach is robust or sensitive to the
knowledge supplied to the model has rarely been discussed. In this paper, we
propose ... | computer science |
1,597 | Neural Symbolic Machines: Learning Semantic Parsers on Freebase with
Weak Supervision (Short Version) | cs.CL | Extending the success of deep neural networks to natural language
understanding and symbolic reasoning requires complex operations and external
memory. Recent neural program induction approaches have attempted to address
this problem, but are typically limited to differentiable memory, and
consequently cannot scale bey... | computer science |
1,598 | Distributional semantics beyond words: Supervised learning of analogy
and paraphrase | cs.LG | There have been several efforts to extend distributional semantics beyond
individual words, to measure the similarity of word pairs, phrases, and
sentences (briefly, tuples; ordered sets of words, contiguous or
noncontiguous). One way to extend beyond words is to compare two tuples using a
function that combines pairwi... | computer science |
1,599 | The Utility of Text: The Case of Amicus Briefs and the Supreme Court | cs.CL | We explore the idea that authoring a piece of text is an act of maximizing
one's expected utility. To make this idea concrete, we consider the societally
important decisions of the Supreme Court of the United States. Extensive past
work in quantitative political science provides a framework for empirically
modeling the... | computer science |
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