676 research outputs found
Parsing Speech: A Neural Approach to Integrating Lexical and Acoustic-Prosodic Information
In conversational speech, the acoustic signal provides cues that help
listeners disambiguate difficult parses. For automatically parsing spoken
utterances, we introduce a model that integrates transcribed text and
acoustic-prosodic features using a convolutional neural network over energy and
pitch trajectories coupled with an attention-based recurrent neural network
that accepts text and prosodic features. We find that different types of
acoustic-prosodic features are individually helpful, and together give
statistically significant improvements in parse and disfluency detection F1
scores over a strong text-only baseline. For this study with known sentence
boundaries, error analyses show that the main benefit of acoustic-prosodic
features is in sentences with disfluencies, attachment decisions are most
improved, and transcription errors obscure gains from prosody.Comment: Accepted in NAACL HLT 201
Unsupervised syntactic chunking with acoustic cues: Computational models for prosodic bootstrapping
Learning to group words into phrases without supervision is a hard task for NLP systems, but infants routinely accomplish it. We hypothesize that infants use acoustic cues to prosody, which NLP systems typically ignore. To evaluate the utility of prosodic information for phrase discovery, we present an HMM-based unsupervised chunker that learns from only transcribed words and raw acoustic correlates to prosody. Unlike previous work on unsupervised parsing and chunking, we use neither gold standard part-of-speech tags nor punctuation in the input. Evaluated on the Switchboard corpus, our model outperforms several baselines that exploit either lexical or prosodic information alone, and, despite producing a flat structure, performs competitively with a state-of-the-art unsupervised lexicalized parser, with a substantial advantage in precision. Our results support the hypothesis that acoustic-prosodic cues provide useful evidence about syntactic phrases for language-learning infants.10 page(s
Computational Language Assessment in patients with speech, language, and communication impairments
Speech, language, and communication symptoms enable the early detection,
diagnosis, treatment planning, and monitoring of neurocognitive disease
progression. Nevertheless, traditional manual neurologic assessment, the speech
and language evaluation standard, is time-consuming and resource-intensive for
clinicians. We argue that Computational Language Assessment (C.L.A.) is an
improvement over conventional manual neurological assessment. Using machine
learning, natural language processing, and signal processing, C.L.A. provides a
neuro-cognitive evaluation of speech, language, and communication in elderly
and high-risk individuals for dementia. ii. facilitates the diagnosis,
prognosis, and therapy efficacy in at-risk and language-impaired populations;
and iii. allows easier extensibility to assess patients from a wide range of
languages. Also, C.L.A. employs Artificial Intelligence models to inform theory
on the relationship between language symptoms and their neural bases. It
significantly advances our ability to optimize the prevention and treatment of
elderly individuals with communication disorders, allowing them to age
gracefully with social engagement.Comment: 36 pages, 2 figures, to be submite
Scientific Information Extraction with Semi-supervised Neural Tagging
This paper addresses the problem of extracting keyphrases from scientific
articles and categorizing them as corresponding to a task, process, or
material. We cast the problem as sequence tagging and introduce semi-supervised
methods to a neural tagging model, which builds on recent advances in named
entity recognition. Since annotated training data is scarce in this domain, we
introduce a graph-based semi-supervised algorithm together with a data
selection scheme to leverage unannotated articles. Both inductive and
transductive semi-supervised learning strategies outperform state-of-the-art
information extraction performance on the 2017 SemEval Task 10 ScienceIE task.Comment: accepted by EMNLP 201
Predictability effects in language acquisition
Human language has two fundamental requirements: it must allow competent speakers
to exchange messages efficiently, and it must be readily learned by children. Recent
work has examined effects of language predictability on language production, with
many researchers arguing that so-called “predictability effects” function towards the
efficiency requirement. Specifically, recent work has found that talkers tend to reduce
linguistic forms that are more probable more heavily. This dissertation proposes the
“Predictability Bootstrapping Hypothesis” that predictability effects also make language
more learnable. There is a great deal of evidence that the adult grammars have
substantial statistical components. Since predictability effects result in heavier reduction
for more probable words and hidden structure, they provide infants with direct
cues to the statistical components of the grammars they are trying to learn.
The corpus studies and computational modeling experiments in this dissertation
show that predictability effects could be a substantial source of information to language-learning
infants, focusing on the potential utility of phonetic reduction in terms of word
duration for syntax acquisition. First, corpora of spontaneous adult-directed and child-directed
speech (ADS and CDS, respectively) are compared to verify that predictability
effects actually exist in CDS. While revealing some differences, mixed effects regressions
on those corpora indicate that predictability effects in CDS are largely similar
(in kind and magnitude) to predictability effects in ADS. This result indicates that predictability
effects are available to infants, however useful they may be.
Second, this dissertation builds probabilistic, unsupervised, and lexicalized models
for learning about syntax from words and durational cues. One series of models is
based on Hidden Markov Models and learns shallow constituency structure, while the
other series is based on the Dependency Model with Valence and learns dependency
structure. These models are then used to measure how useful durational cues are for
syntax acquisition, and to what extent their utility in this task can be attributed to
effects of syntactic predictability on word duration. As part of this investigation, these
models are also used to explore the venerable “Prosodic Bootstrapping Hypothesis”
that prosodic structure, which is cued in part by word duration, may be useful for
syntax acquisition. The empirical evaluations of these models provide evidence that
effects of syntactic predictability on word duration are easier to discover and exploit
than effects of prosodic structure, and that even gold-standard annotations of prosodic
structure provide at most a relatively small improvement in parsing performance over raw word duration.
Taken together, this work indicates that predictability effects provide useful information
about syntax to infants, showing that the Predictability Bootstrapping Hypothesis
for syntax acquisition is computationally plausible and motivating future behavioural
investigation. Additionally, as talkers consider the probability of many different
aspects of linguistic structure when reducing according to predictability effects,
this result also motivates investigation of Predictability Bootstrapping of other aspects
of linguistic knowledge
Unsupervised Chunking with Hierarchical RNN
In Natural Language Processing (NLP), predicting linguistic structures, such
as parsing and chunking, has mostly relied on manual annotations of syntactic
structures. This paper introduces an unsupervised approach to chunking, a
syntactic task that involves grouping words in a non-hierarchical manner. We
present a two-layer Hierarchical Recurrent Neural Network (HRNN) designed to
model word-to-chunk and chunk-to-sentence compositions. Our approach involves a
two-stage training process: pretraining with an unsupervised parser and
finetuning on downstream NLP tasks. Experiments on the CoNLL-2000 dataset
reveal a notable improvement over existing unsupervised methods, enhancing
phrase F1 score by up to 6 percentage points. Further, finetuning with
downstream tasks results in an additional performance improvement.
Interestingly, we observe that the emergence of the chunking structure is
transient during the neural model's downstream-task training. This study
contributes to the advancement of unsupervised syntactic structure discovery
and opens avenues for further research in linguistic theory
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