741 research outputs found
Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational Speech
We describe a statistical approach for modeling dialogue acts in
conversational speech, i.e., speech-act-like units such as Statement, Question,
Backchannel, Agreement, Disagreement, and Apology. Our model detects and
predicts dialogue acts based on lexical, collocational, and prosodic cues, as
well as on the discourse coherence of the dialogue act sequence. The dialogue
model is based on treating the discourse structure of a conversation as a
hidden Markov model and the individual dialogue acts as observations emanating
from the model states. Constraints on the likely sequence of dialogue acts are
modeled via a dialogue act n-gram. The statistical dialogue grammar is combined
with word n-grams, decision trees, and neural networks modeling the
idiosyncratic lexical and prosodic manifestations of each dialogue act. We
develop a probabilistic integration of speech recognition with dialogue
modeling, to improve both speech recognition and dialogue act classification
accuracy. Models are trained and evaluated using a large hand-labeled database
of 1,155 conversations from the Switchboard corpus of spontaneous
human-to-human telephone speech. We achieved good dialogue act labeling
accuracy (65% based on errorful, automatically recognized words and prosody,
and 71% based on word transcripts, compared to a chance baseline accuracy of
35% and human accuracy of 84%) and a small reduction in word recognition error.Comment: 35 pages, 5 figures. Changes in copy editing (note title spelling
changed
When is multitask learning effective? Semantic sequence prediction under varying data conditions
Multitask learning has been applied successfully to a range of tasks, mostly
morphosyntactic. However, little is known on when MTL works and whether there
are data characteristics that help to determine its success. In this paper we
evaluate a range of semantic sequence labeling tasks in a MTL setup. We examine
different auxiliary tasks, amongst which a novel setup, and correlate their
impact to data-dependent conditions. Our results show that MTL is not always
effective, significant improvements are obtained only for 1 out of 5 tasks.
When successful, auxiliary tasks with compact and more uniform label
distributions are preferable.Comment: In EACL 201
EVALUATING DISTRIBUTED WORD REPRESENTATIONS FOR PREDICTING MISSING WORDS IN SENTENCES
In recent years, the distributed representation of words in vector space or word embeddings have become very popular as they have shown significant improvements in many statistical natural language processing (NLP) tasks as compared to traditional language models like Ngram. In this thesis, we explored various state-of-the-art methods like Latent Semantic Analysis, word2vec, and GloVe to learn the distributed representation of words. Their performance was compared based on the accuracy achieved when tasked with selecting the right missing word in the sentence, given five possible options. For this NLP task we trained each of these methods using a training corpus that contained texts of around five hundred 19th century novels from Project Gutenberg. The test set contained 1040 sentences where one word was missing from each sentence. The training and test set were part of the Microsoft Research Sentence Completion Challenge data set. In this work, word vectors obtained by training skip-gram model of word2vec showed the highest accuracy in finding the missing word in the sentences among all the methods tested. We also found that tuning hyperparameters of the models helped in capturing greater syntactic and semantic regularities among words
Semantic Tagging with Deep Residual Networks
We propose a novel semantic tagging task, sem-tagging, tailored for the
purpose of multilingual semantic parsing, and present the first tagger using
deep residual networks (ResNets). Our tagger uses both word and character
representations and includes a novel residual bypass architecture. We evaluate
the tagset both intrinsically on the new task of semantic tagging, as well as
on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an
auxiliary loss function predicting our semantic tags, significantly outperforms
prior results on English Universal Dependencies POS tagging (95.71% accuracy on
UD v1.2 and 95.67% accuracy on UD v1.3).Comment: COLING 2016, camera ready versio
- …