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1,600 | Towards Learning Object Affordance Priors from Technical Texts | cs.LG | Everyday activities performed by artificial assistants can potentially be
executed naively and dangerously given their lack of common sense knowledge.
This paper presents conceptual work towards obtaining prior knowledge on the
usual modality (passive or active) of any given entity, and their affordance
estimates, by e... | computer science |
1,601 | Inferring User Preferences by Probabilistic Logical Reasoning over
Social Networks | cs.SI | We propose a framework for inferring the latent attitudes or preferences of
users by performing probabilistic first-order logical reasoning over the social
network graph. Our method answers questions about Twitter users like {\em Does
this user like sushi?} or {\em Is this user a New York Knicks fan?} by building
a pro... | computer science |
1,602 | Dependency Recurrent Neural Language Models for Sentence Completion | cs.CL | Recent work on language modelling has shifted focus from count-based models
to neural models. In these works, the words in each sentence are always
considered in a left-to-right order. In this paper we show how we can improve
the performance of the recurrent neural network (RNN) language model by
incorporating the synt... | computer science |
1,603 | Dependency-based Convolutional Neural Networks for Sentence Embedding | cs.CL | In sentence modeling and classification, convolutional neural network
approaches have recently achieved state-of-the-art results, but all such
efforts process word vectors sequentially and neglect long-distance
dependencies. To exploit both deep learning and linguistic structures, we
propose a tree-based convolutional ... | computer science |
1,604 | Empirical Study on Deep Learning Models for Question Answering | cs.CL | In this paper we explore deep learning models with memory component or
attention mechanism for question answering task. We combine and compare three
models, Neural Machine Translation, Neural Turing Machine, and Memory Networks
for a simulated QA data set. This paper is the first one that uses Neural
Machine Translatio... | computer science |
1,605 | Deep Reinforcement Learning with a Natural Language Action Space | cs.AI | This paper introduces a novel architecture for reinforcement learning with
deep neural networks designed to handle state and action spaces characterized
by natural language, as found in text-based games. Termed a deep reinforcement
relevance network (DRRN), the architecture represents action and state spaces
with separ... | computer science |
1,606 | Learning with Memory Embeddings | cs.AI | Embedding learning, a.k.a. representation learning, has been shown to be able
to model large-scale semantic knowledge graphs. A key concept is a mapping of
the knowledge graph to a tensor representation whose entries are predicted by
models using latent representations of generalized entities. Latent variable
models ar... | computer science |
1,607 | Building Memory with Concept Learning Capabilities from Large-scale
Knowledge Base | cs.CL | We present a new perspective on neural knowledge base (KB) embeddings, from
which we build a framework that can model symbolic knowledge in the KB together
with its learning process. We show that this framework well regularizes
previous neural KB embedding model for superior performance in reasoning tasks,
while having... | computer science |
1,608 | Thinking Required | cs.LG | There exists a theory of a single general-purpose learning algorithm which
could explain the principles its operation. It assumes the initial rough
architecture, a small library of simple innate circuits which are prewired at
birth. and proposes that all significant mental algorithms are learned. Given
current understa... | computer science |
1,609 | Open challenges in understanding development and evolution of speech
forms: The roles of embodied self-organization, motivation and active
exploration | cs.AI | This article discusses open scientific challenges for understanding
development and evolution of speech forms, as a commentary to Moulin-Frier et
al. (Moulin-Frier et al., 2015). Based on the analysis of mathematical models
of the origins of speech forms, with a focus on their assumptions , we study
the fundamental que... | computer science |
1,610 | Adobe-MIT submission to the DSTC 4 Spoken Language Understanding pilot
task | cs.CL | The Dialog State Tracking Challenge 4 (DSTC 4) proposes several pilot tasks.
In this paper, we focus on the spoken language understanding pilot task, which
consists of tagging a given utterance with speech acts and semantic slots. We
compare different classifiers: the best system obtains 0.52 and 0.67 F1-scores
on the ... | computer science |
1,611 | Towards End-to-End Learning for Dialog State Tracking and Management
using Deep Reinforcement Learning | cs.AI | This paper presents an end-to-end framework for task-oriented dialog systems
using a variant of Deep Recurrent Q-Networks (DRQN). The model is able to
interface with a relational database and jointly learn policies for both
language understanding and dialog strategy. Moreover, we propose a hybrid
algorithm that combine... | computer science |
1,612 | Deep Reinforcement Learning with a Combinatorial Action Space for
Predicting Popular Reddit Threads | cs.CL | We introduce an online popularity prediction and tracking task as a benchmark
task for reinforcement learning with a combinatorial, natural language action
space. A specified number of discussion threads predicted to be popular are
recommended, chosen from a fixed window of recent comments to track. Novel deep
reinforc... | computer science |
1,613 | Neural Belief Tracker: Data-Driven Dialogue State Tracking | cs.CL | One of the core components of modern spoken dialogue systems is the belief
tracker, which estimates the user's goal at every step of the dialogue.
However, most current approaches have difficulty scaling to larger, more
complex dialogue domains. This is due to their dependency on either: a) Spoken
Language Understandin... | computer science |
1,614 | The LAMBADA dataset: Word prediction requiring a broad discourse context | cs.CL | We introduce LAMBADA, a dataset to evaluate the capabilities of computational
models for text understanding by means of a word prediction task. LAMBADA is a
collection of narrative passages sharing the characteristic that human subjects
are able to guess their last word if they are exposed to the whole passage, but
not... | computer science |
1,615 | Unanimous Prediction for 100% Precision with Application to Learning
Semantic Mappings | cs.LG | Can we train a system that, on any new input, either says "don't know" or
makes a prediction that is guaranteed to be correct? We answer the question in
the affirmative provided our model family is well-specified. Specifically, we
introduce the unanimity principle: only predict when all models consistent with
the train... | computer science |
1,616 | Lifted Rule Injection for Relation Embeddings | cs.LG | Methods based on representation learning currently hold the state-of-the-art
in many natural language processing and knowledge base inference tasks. Yet, a
major challenge is how to efficiently incorporate commonsense knowledge into
such models. A recent approach regularizes relation and entity representations
by propo... | computer science |
1,617 | Learning Relational Dependency Networks for Relation Extraction | cs.AI | We consider the task of KBP slot filling -- extracting relation information
from newswire documents for knowledge base construction. We present our
pipeline, which employs Relational Dependency Networks (RDNs) to learn
linguistic patterns for relation extraction. Additionally, we demonstrate how
several components such... | computer science |
1,618 | Cognitive Science in the era of Artificial Intelligence: A roadmap for
reverse-engineering the infant language-learner | cs.CL | During their first years of life, infants learn the language(s) of their
environment at an amazing speed despite large cross cultural variations in
amount and complexity of the available language input. Understanding this
simple fact still escapes current cognitive and linguistic theories. Recently,
spectacular progres... | computer science |
1,619 | Unsupervised, Efficient and Semantic Expertise Retrieval | cs.IR | We introduce an unsupervised discriminative model for the task of retrieving
experts in online document collections. We exclusively employ textual evidence
and avoid explicit feature engineering by learning distributed word
representations in an unsupervised way. We compare our model to
state-of-the-art unsupervised st... | computer science |
1,620 | Semantics derived automatically from language corpora contain human-like
biases | cs.AI | Artificial intelligence and machine learning are in a period of astounding
growth. However, there are concerns that these technologies may be used, either
with or without intention, to perpetuate the prejudice and unfairness that
unfortunately characterizes many human institutions. Here we show for the first
time that ... | computer science |
1,621 | Wav2Letter: an End-to-End ConvNet-based Speech Recognition System | cs.LG | This paper presents a simple end-to-end model for speech recognition,
combining a convolutional network based acoustic model and a graph decoding. It
is trained to output letters, with transcribed speech, without the need for
force alignment of phonemes. We introduce an automatic segmentation criterion
for training fro... | computer science |
1,622 | Weakly Supervised PLDA Training | cs.LG | PLDA is a popular normalization approach for the i-vector model, and it has
delivered state-of-the-art performance in speaker verification. However, PLDA
training requires a large amount of labelled development data, which is highly
expensive in most cases. We present a cheap PLDA training approach, which
assumes that ... | computer science |
1,623 | Personalizing a Dialogue System with Transfer Reinforcement Learning | cs.AI | It is difficult to train a personalized task-oriented dialogue system because
the data collected from each individual is often insufficient. Personalized
dialogue systems trained on a small dataset can overfit and make it difficult
to adapt to different user needs. One way to solve this problem is to consider
a collect... | computer science |
1,624 | Neural Symbolic Machines: Learning Semantic Parsers on Freebase with
Weak Supervision | cs.CL | Harnessing the statistical power of neural networks to perform language
understanding and symbolic reasoning is difficult, when it requires executing
efficient discrete operations against a large knowledge-base. In this work, we
introduce a Neural Symbolic Machine, which contains (a) a neural "programmer",
i.e., a sequ... | computer science |
1,625 | Sentence Ordering and Coherence Modeling using Recurrent Neural Networks | cs.CL | Modeling the structure of coherent texts is a key NLP problem. The task of
coherently organizing a given set of sentences has been commonly used to build
and evaluate models that understand such structure. We propose an end-to-end
unsupervised deep learning approach based on the set-to-sequence framework to
address thi... | computer science |
1,626 | UTCNN: a Deep Learning Model of Stance Classificationon on Social Media
Text | cs.CL | Most neural network models for document classification on social media focus
on text infor-mation to the neglect of other information on these platforms. In
this paper, we classify post stance on social media channels and develop UTCNN,
a neural network model that incorporates user tastes, topic tastes, and user
commen... | computer science |
1,627 | Traversing Knowledge Graph in Vector Space without Symbolic Space
Guidance | cs.AI | Recent studies on knowledge base completion, the task of recovering missing
facts based on observed facts, demonstrate the importance of learning
embeddings from multi-step relations. Due to the size of knowledge bases,
previous works manually design relation paths of observed triplets in symbolic
space (e.g. random wa... | computer science |
1,628 | A Multichannel Convolutional Neural Network For Cross-language Dialog
State Tracking | cs.CL | The fifth Dialog State Tracking Challenge (DSTC5) introduces a new
cross-language dialog state tracking scenario, where the participants are asked
to build their trackers based on the English training corpus, while evaluating
them with the unlabeled Chinese corpus. Although the computer-generated
translations for both ... | computer science |
1,629 | CommAI: Evaluating the first steps towards a useful general AI | cs.LG | With machine learning successfully applied to new daunting problems almost
every day, general AI starts looking like an attainable goal. However, most
current research focuses instead on important but narrow applications, such as
image classification or machine translation. We believe this to be largely due
to the lack... | computer science |
1,630 | Representations of language in a model of visually grounded speech
signal | cs.CL | We present a visually grounded model of speech perception which projects
spoken utterances and images to a joint semantic space. We use a multi-layer
recurrent highway network to model the temporal nature of spoken speech, and
show that it learns to extract both form and meaning-based linguistic knowledge
from the inpu... | computer science |
1,631 | Maximum-Likelihood Augmented Discrete Generative Adversarial Networks | cs.AI | Despite the successes in capturing continuous distributions, the application
of generative adversarial networks (GANs) to discrete settings, like natural
language tasks, is rather restricted. The fundamental reason is the difficulty
of back-propagation through discrete random variables combined with the
inherent instab... | computer science |
1,632 | Convolutional Recurrent Neural Networks for Small-Footprint Keyword
Spotting | cs.CL | Keyword spotting (KWS) constitutes a major component of human-technology
interfaces. Maximizing the detection accuracy at a low false alarm (FA) rate,
while minimizing the footprint size, latency and complexity are the goals for
KWS. Towards achieving them, we study Convolutional Recurrent Neural Networks
(CRNNs). Insp... | computer science |
1,633 | Investigation of Language Understanding Impact for Reinforcement
Learning Based Dialogue Systems | cs.CL | Language understanding is a key component in a spoken dialogue system. In
this paper, we investigate how the language understanding module influences the
dialogue system performance by conducting a series of systematic experiments on
a task-oriented neural dialogue system in a reinforcement learning based
setting. The ... | computer science |
1,634 | Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep
Reinforcement Learning | cs.CL | Building a dialogue agent to fulfill complex tasks, such as travel planning,
is challenging because the agent has to learn to collectively complete multiple
subtasks. For example, the agent needs to reserve a hotel and book a flight so
that there leaves enough time for commute between arrival and hotel check-in.
This p... | computer science |
1,635 | Optimizing Differentiable Relaxations of Coreference Evaluation Metrics | cs.CL | Coreference evaluation metrics are hard to optimize directly as they are
non-differentiable functions, not easily decomposable into elementary
decisions. Consequently, most approaches optimize objectives only indirectly
related to the end goal, resulting in suboptimal performance. Instead, we
propose a differentiable r... | computer science |
1,636 | A Large Self-Annotated Corpus for Sarcasm | cs.CL | We introduce the Self-Annotated Reddit Corpus (SARC), a large corpus for
sarcasm research and for training and evaluating systems for sarcasm detection.
The corpus has 1.3 million sarcastic statements -- 10 times more than any
previous dataset -- and many times more instances of non-sarcastic statements,
allowing for l... | computer science |
1,637 | An Interpretable Knowledge Transfer Model for Knowledge Base Completion | cs.CL | Knowledge bases are important resources for a variety of natural language
processing tasks but suffer from incompleteness. We propose a novel embedding
model, \emph{ITransF}, to perform knowledge base completion. Equipped with a
sparse attention mechanism, ITransF discovers hidden concepts of relations and
transfer sta... | computer science |
1,638 | Naturalizing a Programming Language via Interactive Learning | cs.CL | Our goal is to create a convenient natural language interface for performing
well-specified but complex actions such as analyzing data, manipulating text,
and querying databases. However, existing natural language interfaces for such
tasks are quite primitive compared to the power one wields with a programming
language... | computer science |
1,639 | Tree-Structured Neural Machine for Linguistics-Aware Sentence Generation | cs.AI | Different from other sequential data, sentences in natural language are
structured by linguistic grammars. Previous generative conversational models
with chain-structured decoder ignore this structure in human language and might
generate plausible responses with less satisfactory relevance and fluency. In
this study, w... | computer science |
1,640 | Analogical Inference for Multi-Relational Embeddings | cs.LG | Large-scale multi-relational embedding refers to the task of learning the
latent representations for entities and relations in large knowledge graphs. An
effective and scalable solution for this problem is crucial for the true
success of knowledge-based inference in a broad range of applications. This
paper proposes a ... | computer science |
1,641 | Program Induction by Rationale Generation : Learning to Solve and
Explain Algebraic Word Problems | cs.AI | Solving algebraic word problems requires executing a series of arithmetic
operations---a program---to obtain a final answer. However, since programs can
be arbitrarily complicated, inducing them directly from question-answer pairs
is a formidable challenge. To make this task more feasible, we solve these
problems by ge... | computer science |
1,642 | Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs | cs.AI | The availability of large scale event data with time stamps has given rise to
dynamically evolving knowledge graphs that contain temporal information for
each edge. Reasoning over time in such dynamic knowledge graphs is not yet well
understood. To this end, we present Know-Evolve, a novel deep evolutionary
knowledge n... | computer science |
1,643 | Search Engine Guided Non-Parametric Neural Machine Translation | cs.CL | In this paper, we extend an attention-based neural machine translation (NMT)
model by allowing it to access an entire training set of parallel sentence
pairs even after training. The proposed approach consists of two stages. In the
first stage--retrieval stage--, an off-the-shelf, black-box search engine is
used to ret... | computer science |
1,644 | Mixed Membership Word Embeddings for Computational Social Science | cs.CL | Word embeddings improve the performance of NLP systems by revealing the
hidden structural relationships between words. Despite their success in many
applications, word embeddings have seen very little use in computational social
science NLP tasks, presumably due to their reliance on big data, and to a lack
of interpret... | computer science |
1,645 | Discovering Discrete Latent Topics with Neural Variational Inference | cs.CL | Topic models have been widely explored as probabilistic generative models of
documents. Traditional inference methods have sought closed-form derivations
for updating the models, however as the expressiveness of these models grows,
so does the difficulty of performing fast and accurate inference over their
parameters. ... | computer science |
1,646 | Zero-Shot Relation Extraction via Reading Comprehension | cs.CL | We show that relation extraction can be reduced to answering simple reading
comprehension questions, by associating one or more natural-language questions
with each relation slot. This reduction has several advantages: we can (1)
learn relation-extraction models by extending recent neural
reading-comprehension techniqu... | computer science |
1,647 | Joint Extraction of Entities and Relations Based on a Novel Tagging
Scheme | cs.CL | Joint extraction of entities and relations is an important task in
information extraction. To tackle this problem, we firstly propose a novel
tagging scheme that can convert the joint extraction task to a tagging problem.
Then, based on our tagging scheme, we study different end-to-end models to
extract entities and th... | computer science |
1,648 | Gated-Attention Architectures for Task-Oriented Language Grounding | cs.LG | To perform tasks specified by natural language instructions, autonomous
agents need to extract semantically meaningful representations of language and
map it to visual elements and actions in the environment. This problem is
called task-oriented language grounding. We propose an end-to-end trainable
neural architecture... | computer science |
1,649 | Representation Learning for Grounded Spatial Reasoning | cs.CL | The interpretation of spatial references is highly contextual, requiring
joint inference over both language and the environment. We consider the task of
spatial reasoning in a simulated environment, where an agent can act and
receive rewards. The proposed model learns a representation of the world
steered by instructio... | computer science |
1,650 | The Role of Conversation Context for Sarcasm Detection in Online
Interactions | cs.CL | Computational models for sarcasm detection have often relied on the content
of utterances in isolation. However, speaker's sarcastic intent is not always
obvious without additional context. Focusing on social media discussions, we
investigate two issues: (1) does modeling of conversation context help in
sarcasm detecti... | computer science |
1,651 | Learned in Translation: Contextualized Word Vectors | cs.CL | Computer vision has benefited from initializing multiple deep layers with
weights pretrained on large supervised training sets like ImageNet. Natural
language processing (NLP) typically sees initialization of only the lowest
layer of deep models with pretrained word vectors. In this paper, we use a deep
LSTM encoder fr... | computer science |
1,652 | Deep Transfer in Reinforcement Learning by Language Grounding | cs.CL | In this paper, we explore the utilization of natural language to drive
transfer for reinforcement learning (RL). Despite the wide-spread application
of deep RL techniques, learning generalized policy representations that work
across domains remains a challenging problem. We demonstrate that textual
descriptions of envi... | computer science |
1,653 | e-QRAQ: A Multi-turn Reasoning Dataset and Simulator with Explanations | cs.LG | In this paper we present a new dataset and user simulator e-QRAQ (explainable
Query, Reason, and Answer Question) which tests an Agent's ability to read an
ambiguous text; ask questions until it can answer a challenge question; and
explain the reasoning behind its questions and answer. The User simulator
provides the A... | computer science |
1,654 | Shortcut-Stacked Sentence Encoders for Multi-Domain Inference | cs.CL | We present a simple sequential sentence encoder for multi-domain natural
language inference. Our encoder is based on stacked bidirectional LSTM-RNNs
with shortcut connections and fine-tuning of word embeddings. The overall
supervised model uses the above encoder to encode two input sentences into two
vectors, and then ... | computer science |
1,655 | Learning how to Active Learn: A Deep Reinforcement Learning Approach | cs.CL | Active learning aims to select a small subset of data for annotation such
that a classifier learned on the data is highly accurate. This is usually done
using heuristic selection methods, however the effectiveness of such methods is
limited and moreover, the performance of heuristics varies between datasets. To
address... | computer science |
1,656 | LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online
Auctions | cs.LG | We present LADDER, the first deep reinforcement learning agent that can
successfully learn control policies for large-scale real-world problems
directly from raw inputs composed of high-level semantic information. The agent
is based on an asynchronous stochastic variant of DQN (Deep Q Network) named
DASQN. The inputs o... | computer science |
1,657 | Towards an Automatic Turing Test: Learning to Evaluate Dialogue
Responses | cs.CL | Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. Unfortunately, existing automatic evaluation
metrics are biased and correlate very poorly with human judgements of response
quality. Yet having an accurate automatic evaluation procedure is crucial for
dialogue... | computer science |
1,658 | Learning what to read: Focused machine reading | cs.AI | Recent efforts in bioinformatics have achieved tremendous progress in the
machine reading of biomedical literature, and the assembly of the extracted
biochemical interactions into large-scale models such as protein signaling
pathways. However, batch machine reading of literature at today's scale (PubMed
alone indexes o... | computer science |
1,659 | Refining Source Representations with Relation Networks for Neural
Machine Translation | cs.CL | Although neural machine translation (NMT) with the encoder-decoder framework
has achieved great success in recent times, it still suffers from some
drawbacks: RNNs tend to forget old information which is often useful and the
encoder only operates through words without considering word relationship. To
solve these probl... | computer science |
1,660 | Variational Reasoning for Question Answering with Knowledge Graph | cs.LG | Knowledge graph (KG) is known to be helpful for the task of question
answering (QA), since it provides well-structured relational information
between entities, and allows one to further infer indirect facts. However, it
is challenging to build QA systems which can learn to reason over knowledge
graphs based on question... | computer science |
1,661 | HDLTex: Hierarchical Deep Learning for Text Classification | cs.LG | The continually increasing number of documents produced each year
necessitates ever improving information processing methods for searching,
retrieving, and organizing text. Central to these information processing
methods is document classification, which has become an important application
for supervised learning. Rece... | computer science |
1,662 | Long Text Generation via Adversarial Training with Leaked Information | cs.CL | Automatically generating coherent and semantically meaningful text has many
applications in machine translation, dialogue systems, image captioning, etc.
Recently, by combining with policy gradient, Generative Adversarial Nets (GAN)
that use a discriminative model to guide the training of the generative model
as a rein... | computer science |
1,663 | Training an adaptive dialogue policy for interactive learning of
visually grounded word meanings | cs.CL | We present a multi-modal dialogue system for interactive learning of
perceptually grounded word meanings from a human tutor. The system integrates
an incremental, semantic parsing/generation framework - Dynamic Syntax and Type
Theory with Records (DS-TTR) - with a set of visual classifiers that are
learned throughout t... | computer science |
1,664 | The BURCHAK corpus: a Challenge Data Set for Interactive Learning of
Visually Grounded Word Meanings | cs.CL | We motivate and describe a new freely available human-human dialogue dataset
for interactive learning of visually grounded word meanings through ostensive
definition by a tutor to a learner. The data has been collected using a novel,
character-by-character variant of the DiET chat tool (Healey et al., 2003;
Mills and H... | computer science |
1,665 | Unsupervised Neural Machine Translation | cs.CL | In spite of the recent success of neural machine translation (NMT) in
standard benchmarks, the lack of large parallel corpora poses a major practical
problem for many language pairs. There have been several proposals to alleviate
this issue with, for instance, triangulation and semi-supervised learning
techniques, but ... | computer science |
1,666 | Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue
Policy Learning | cs.CL | This paper presents a new method --- adversarial advantage actor-critic
(Adversarial A2C), which significantly improves the efficiency of dialogue
policy learning in task-completion dialogue systems. Inspired by generative
adversarial networks (GAN), we train a discriminator to differentiate
responses/actions generated... | computer science |
1,667 | Question Asking as Program Generation | cs.CL | A hallmark of human intelligence is the ability to ask rich, creative, and
revealing questions. Here we introduce a cognitive model capable of
constructing human-like questions. Our approach treats questions as formal
programs that, when executed on the state of the world, output an answer. The
model specifies a probab... | computer science |
1,668 | Generating Thematic Chinese Poetry using Conditional Variational
Autoencoders with Hybrid Decoders | cs.CL | Computer poetry generation is our first step towards computer writing.
Writing must have a theme. The current approaches of using sequence-to-sequence
models with attention often produce non-thematic poems. We present a novel
conditional variational autoencoder with a hybrid decoder adding the
deconvolutional neural ne... | computer science |
1,669 | Improved Neural Text Attribute Transfer with Non-parallel Data | cs.CL | Text attribute transfer using non-parallel data requires methods that can
perform disentanglement of content and linguistic attributes. In this work, we
propose multiple improvements over the existing approaches that enable the
encoder-decoder framework to cope with the text attribute transfer from
non-parallel data. W... | computer science |
1,670 | Complex Structure Leads to Overfitting: A Structure Regularization
Decoding Method for Natural Language Processing | cs.LG | Recent systems on structured prediction focus on increasing the level of
structural dependencies within the model. However, our study suggests that
complex structures entail high overfitting risks. To control the
structure-based overfitting, we propose to conduct structure regularization
decoding (SR decoding). The dec... | computer science |
1,671 | Embedding Words as Distributions with a Bayesian Skip-gram Model | cs.CL | We introduce a method for embedding words as probability densities in a
low-dimensional space. Rather than assuming that a word embedding is fixed
across the entire text collection, as in standard word embedding methods, in
our Bayesian model we generate it from a word-specific prior density for each
occurrence of a gi... | computer science |
1,672 | End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy
Gradient | cs.AI | Learning a goal-oriented dialog policy is generally performed offline with
supervised learning algorithms or online with reinforcement learning (RL).
Additionally, as companies accumulate massive quantities of dialog transcripts
between customers and trained human agents, encoder-decoder methods have gained
popularity ... | computer science |
1,673 | Rasa: Open Source Language Understanding and Dialogue Management | cs.CL | We introduce a pair of tools, Rasa NLU and Rasa Core, which are open source
python libraries for building conversational software. Their purpose is to make
machine-learning based dialogue management and language understanding
accessible to non-specialist software developers. In terms of design
philosophy, we aim for ea... | computer science |
1,674 | Sentiment Predictability for Stocks | cs.CL | In this work, we present our findings and experiments for stock-market
prediction using various textual sentiment analysis tools, such as mood
analysis and event extraction, as well as prediction models, such as LSTMs and
specific convolutional architectures. | computer science |
1,675 | Multi-Task Pharmacovigilance Mining from Social Media Posts | cs.LG | Social media has grown to be a crucial information source for
pharmacovigilance studies where an increasing number of people post adverse
reactions to medical drugs that are previously unreported. Aiming to
effectively monitor various aspects of Adverse Drug Reactions (ADRs) from
diversely expressed social medical post... | computer science |
1,676 | Evaluating approaches for supervised semantic labeling | cs.LG | Relational data sources are still one of the most popular ways to store
enterprise or Web data, however, the issue with relational schema is the lack
of a well-defined semantic description. A common ontology provides a way to
represent the meaning of a relational schema and can facilitate the integration
of heterogeneo... | computer science |
1,677 | Improving Variational Encoder-Decoders in Dialogue Generation | cs.CL | Variational encoder-decoders (VEDs) have shown promising results in dialogue
generation. However, the latent variable distributions are usually approximated
by a much simpler model than the powerful RNN structure used for encoding and
decoding, yielding the KL-vanishing problem and inconsistent training
objective. In t... | computer science |
1,678 | An efficient framework for learning sentence representations | cs.CL | In this work we propose a simple and efficient framework for learning
sentence representations from unlabelled data. Drawing inspiration from the
distributional hypothesis and recent work on learning sentence representations,
we reformulate the problem of predicting the context in which a sentence
appears as a classifi... | computer science |
1,679 | SpCoSLAM 2.0: An Improved and Scalable Online Learning of Spatial
Concepts and Language Models with Mapping | cs.RO | In this paper, we propose a novel online learning algorithm, SpCoSLAM 2.0 for
spatial concepts and lexical acquisition with higher accuracy and scalability.
In previous work, we proposed SpCoSLAM as an online learning algorithm based on
the Rao--Blackwellized particle filter. However, this conventional algorithm
had pr... | computer science |
1,680 | The Web as a Knowledge-base for Answering Complex Questions | cs.CL | Answering complex questions is a time-consuming activity for humans that
requires reasoning and integration of information. Recent work on reading
comprehension made headway in answering simple questions, but tackling complex
questions is still an ongoing research challenge. Conversely, semantic parsers
have been succe... | computer science |
1,681 | Neural Text Generation: Past, Present and Beyond | cs.CL | This paper presents a systematic survey on recent development of neural text
generation models. Specifically, we start from recurrent neural network
language models with the traditional maximum likelihood estimation training
scheme and point out its shortcoming for text generation. We thus introduce the
recently propos... | computer science |
1,682 | Likelihood-based semi-supervised model selection with applications to
speech processing | stat.ML | In conventional supervised pattern recognition tasks, model selection is
typically accomplished by minimizing the classification error rate on a set of
so-called development data, subject to ground-truth labeling by human experts
or some other means. In the context of speech processing systems and other
large-scale pra... | computer science |
1,683 | Inference by Minimizing Size, Divergence, or their Sum | cs.LG | We speed up marginal inference by ignoring factors that do not significantly
contribute to overall accuracy. In order to pick a suitable subset of factors
to ignore, we propose three schemes: minimizing the number of model factors
under a bound on the KL divergence between pruned and full models; minimizing
the KL dive... | computer science |
1,684 | Concept Modeling with Superwords | stat.ML | In information retrieval, a fundamental goal is to transform a document into
concepts that are representative of its content. The term "representative" is
in itself challenging to define, and various tasks require different
granularities of concepts. In this paper, we aim to model concepts that are
sparse over the voca... | computer science |
1,685 | The Expressive Power of Word Embeddings | cs.LG | We seek to better understand the difference in quality of the several
publicly released embeddings. We propose several tasks that help to distinguish
the characteristics of different embeddings. Our evaluation of sentiment
polarity and synonym/antonym relations shows that embeddings are able to
capture surprisingly nua... | computer science |
1,686 | Transfer Topic Modeling with Ease and Scalability | cs.CL | The increasing volume of short texts generated on social media sites, such as
Twitter or Facebook, creates a great demand for effective and efficient topic
modeling approaches. While latent Dirichlet allocation (LDA) can be applied, it
is not optimal due to its weakness in handling short texts with fast-changing
topics... | computer science |
1,687 | Learning Mixtures of Submodular Shells with Application to Document
Summarization | cs.LG | We introduce a method to learn a mixture of submodular "shells" in a
large-margin setting. A submodular shell is an abstract submodular function
that can be instantiated with a ground set and a set of parameters to produce a
submodular function. A mixture of such shells can then also be so instantiated
to produce a mor... | computer science |
1,688 | Learning Word Representations with Hierarchical Sparse Coding | cs.CL | We propose a new method for learning word representations using hierarchical
regularization in sparse coding inspired by the linguistic study of word
meanings. We show an efficient learning algorithm based on stochastic proximal
methods that is significantly faster than previous approaches, making it
possible to perfor... | computer science |
1,689 | Modelling, Visualising and Summarising Documents with a Single
Convolutional Neural Network | cs.CL | Capturing the compositional process which maps the meaning of words to that
of documents is a central challenge for researchers in Natural Language
Processing and Information Retrieval. We introduce a model that is able to
represent the meaning of documents by embedding them in a low dimensional
vector space, while pre... | computer science |
1,690 | Authorship Attribution through Function Word Adjacency Networks | cs.CL | A method for authorship attribution based on function word adjacency networks
(WANs) is introduced. Function words are parts of speech that express
grammatical relationships between other words but do not carry lexical meaning
on their own. In the WANs in this paper, nodes are function words and directed
edges stand in... | computer science |
1,691 | Multilingual Topic Models for Unaligned Text | cs.CL | We develop the multilingual topic model for unaligned text (MuTo), a
probabilistic model of text that is designed to analyze corpora composed of
documents in two languages. From these documents, MuTo uses stochastic EM to
simultaneously discover both a matching between the languages and multilingual
latent topics. We d... | computer science |
1,692 | Integrating Document Clustering and Topic Modeling | cs.LG | Document clustering and topic modeling are two closely related tasks which
can mutually benefit each other. Topic modeling can project documents into a
topic space which facilitates effective document clustering. Cluster labels
discovered by document clustering can be incorporated into topic models to
extract local top... | computer science |
1,693 | Nonparametric Spherical Topic Modeling with Word Embeddings | cs.CL | Traditional topic models do not account for semantic regularities in
language. Recent distributional representations of words exhibit semantic
consistency over directional metrics such as cosine similarity. However,
neither categorical nor Gaussian observational distributions used in existing
topic models are appropria... | computer science |
1,694 | Combinatorial Topic Models using Small-Variance Asymptotics | cs.LG | Topic models have emerged as fundamental tools in unsupervised machine
learning. Most modern topic modeling algorithms take a probabilistic view and
derive inference algorithms based on Latent Dirichlet Allocation (LDA) or its
variants. In contrast, we study topic modeling as a combinatorial optimization
problem, and p... | computer science |
1,695 | Multidimensional counting grids: Inferring word order from disordered
bags of words | cs.IR | Models of bags of words typically assume topic mixing so that the words in a
single bag come from a limited number of topics. We show here that many sets of
bag of words exhibit a very different pattern of variation than the patterns
that are efficiently captured by topic mixing. In many cases, from one bag of
words to... | computer science |
1,696 | Latent Topic Models for Hypertext | cs.IR | Latent topic models have been successfully applied as an unsupervised topic
discovery technique in large document collections. With the proliferation of
hypertext document collection such as the Internet, there has also been great
interest in extending these approaches to hypertext [6, 9]. These approaches
typically mo... | computer science |
1,697 | Learning to Identify Regular Expressions that Describe Email Campaigns | cs.LG | This paper addresses the problem of inferring a regular expression from a
given set of strings that resembles, as closely as possible, the regular
expression that a human expert would have written to identify the language.
This is motivated by our goal of automating the task of postmasters of an email
service who use r... | computer science |
1,698 | Learning Multilingual Word Representations using a Bag-of-Words
Autoencoder | cs.CL | Recent work on learning multilingual word representations usually relies on
the use of word-level alignements (e.g. infered with the help of GIZA++)
between translated sentences, in order to align the word embeddings in
different languages. In this workshop paper, we investigate an autoencoder
model for learning multil... | computer science |
1,699 | Parsimonious Topic Models with Salient Word Discovery | cs.LG | We propose a parsimonious topic model for text corpora. In related models
such as Latent Dirichlet Allocation (LDA), all words are modeled
topic-specifically, even though many words occur with similar frequencies
across different topics. Our modeling determines salient words for each topic,
which have topic-specific pr... | computer science |
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