13,394 research outputs found
Investigating Linguistic Pattern Ordering in Hierarchical Natural Language Generation
Natural language generation (NLG) is a critical component in spoken dialogue
system, which can be divided into two phases: (1) sentence planning: deciding
the overall sentence structure, (2) surface realization: determining specific
word forms and flattening the sentence structure into a string. With the rise
of deep learning, most modern NLG models are based on a sequence-to-sequence
(seq2seq) model, which basically contains an encoder-decoder structure; these
NLG models generate sentences from scratch by jointly optimizing sentence
planning and surface realization. However, such simple encoder-decoder
architecture usually fail to generate complex and long sentences, because the
decoder has difficulty learning all grammar and diction knowledge well. This
paper introduces an NLG model with a hierarchical attentional decoder, where
the hierarchy focuses on leveraging linguistic knowledge in a specific order.
The experiments show that the proposed method significantly outperforms the
traditional seq2seq model with a smaller model size, and the design of the
hierarchical attentional decoder can be applied to various NLG systems.
Furthermore, different generation strategies based on linguistic patterns are
investigated and analyzed in order to guide future NLG research work.Comment: accepted by the 7th IEEE Workshop on Spoken Language Technology (SLT
2018). arXiv admin note: text overlap with arXiv:1808.0274
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
A Hierarchical Neural Autoencoder for Paragraphs and Documents
Natural language generation of coherent long texts like paragraphs or longer
documents is a challenging problem for recurrent networks models. In this
paper, we explore an important step toward this generation task: training an
LSTM (Long-short term memory) auto-encoder to preserve and reconstruct
multi-sentence paragraphs. We introduce an LSTM model that hierarchically
builds an embedding for a paragraph from embeddings for sentences and words,
then decodes this embedding to reconstruct the original paragraph. We evaluate
the reconstructed paragraph using standard metrics like ROUGE and Entity Grid,
showing that neural models are able to encode texts in a way that preserve
syntactic, semantic, and discourse coherence. While only a first step toward
generating coherent text units from neural models, our work has the potential
to significantly impact natural language generation and
summarization\footnote{Code for the three models described in this paper can be
found at www.stanford.edu/~jiweil/
Translating Phrases in Neural Machine Translation
Phrases play an important role in natural language understanding and machine
translation (Sag et al., 2002; Villavicencio et al., 2005). However, it is
difficult to integrate them into current neural machine translation (NMT) which
reads and generates sentences word by word. In this work, we propose a method
to translate phrases in NMT by integrating a phrase memory storing target
phrases from a phrase-based statistical machine translation (SMT) system into
the encoder-decoder architecture of NMT. At each decoding step, the phrase
memory is first re-written by the SMT model, which dynamically generates
relevant target phrases with contextual information provided by the NMT model.
Then the proposed model reads the phrase memory to make probability estimations
for all phrases in the phrase memory. If phrase generation is carried on, the
NMT decoder selects an appropriate phrase from the memory to perform phrase
translation and updates its decoding state by consuming the words in the
selected phrase. Otherwise, the NMT decoder generates a word from the
vocabulary as the general NMT decoder does. Experiment results on the Chinese
to English translation show that the proposed model achieves significant
improvements over the baseline on various test sets.Comment: Accepted by EMNLP 201
Assigning personality/identity to a chatting machine for coherent conversation generation
Endowing a chatbot with personality or an identity is quite challenging but
critical to deliver more realistic and natural conversations. In this paper, we
address the issue of generating responses that are coherent to a pre-specified
agent profile. We design a model consisting of three modules: a profile
detector to decide whether a post should be responded using the profile and
which key should be addressed, a bidirectional decoder to generate responses
forward and backward starting from a selected profile value, and a position
detector that predicts a word position from which decoding should start given a
selected profile value. We show that general conversation data from social
media can be used to generate profile-coherent responses. Manual and automatic
evaluation shows that our model can deliver more coherent, natural, and
diversified responses.Comment: an error on author informatio
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
Perception and expression of emotion are key factors to the success of
dialogue systems or conversational agents. However, this problem has not been
studied in large-scale conversation generation so far. In this paper, we
propose Emotional Chatting Machine (ECM) that can generate appropriate
responses not only in content (relevant and grammatical) but also in emotion
(emotionally consistent). To the best of our knowledge, this is the first work
that addresses the emotion factor in large-scale conversation generation. ECM
addresses the factor using three new mechanisms that respectively (1) models
the high-level abstraction of emotion expressions by embedding emotion
categories, (2) captures the change of implicit internal emotion states, and
(3) uses explicit emotion expressions with an external emotion vocabulary.
Experiments show that the proposed model can generate responses appropriate not
only in content but also in emotion.Comment: Accepted in AAAI 201
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