8,285 research outputs found
Better Conversations by Modeling,Filtering,and Optimizing for Coherence and Diversity
We present three enhancements to existing encoder-decoder models for
open-domain conversational agents, aimed at effectively modeling coherence and
promoting output diversity: (1) We introduce a measure of coherence as the
GloVe embedding similarity between the dialogue context and the generated
response, (2) we filter our training corpora based on the measure of coherence
to obtain topically coherent and lexically diverse context-response pairs, (3)
we then train a response generator using a conditional variational autoencoder
model that incorporates the measure of coherence as a latent variable and uses
a context gate to guarantee topical consistency with the context and promote
lexical diversity. Experiments on the OpenSubtitles corpus show a substantial
improvement over competitive neural models in terms of BLEU score as well as
metrics of coherence and diversity
Modelling Grocery Retail Topic Distributions: Evaluation, Interpretability and Stability
Understanding the shopping motivations behind market baskets has high
commercial value in the grocery retail industry. Analyzing shopping
transactions demands techniques that can cope with the volume and
dimensionality of grocery transactional data while keeping interpretable
outcomes. Latent Dirichlet Allocation (LDA) provides a suitable framework to
process grocery transactions and to discover a broad representation of
customers' shopping motivations. However, summarizing the posterior
distribution of an LDA model is challenging, while individual LDA draws may not
be coherent and cannot capture topic uncertainty. Moreover, the evaluation of
LDA models is dominated by model-fit measures which may not adequately capture
the qualitative aspects such as interpretability and stability of topics.
In this paper, we introduce clustering methodology that post-processes
posterior LDA draws to summarise the entire posterior distribution and identify
semantic modes represented as recurrent topics. Our approach is an alternative
to standard label-switching techniques and provides a single posterior summary
set of topics, as well as associated measures of uncertainty. Furthermore, we
establish a more holistic definition for model evaluation, which assesses topic
models based not only on their likelihood but also on their coherence,
distinctiveness and stability. By means of a survey, we set thresholds for the
interpretation of topic coherence and topic similarity in the domain of grocery
retail data. We demonstrate that the selection of recurrent topics through our
clustering methodology not only improves model likelihood but also outperforms
the qualitative aspects of LDA such as interpretability and stability. We
illustrate our methods on an example from a large UK supermarket chain.Comment: 20 pages, 9 figure
Combining Thesaurus Knowledge and Probabilistic Topic Models
In this paper we present the approach of introducing thesaurus knowledge into
probabilistic topic models. The main idea of the approach is based on the
assumption that the frequencies of semantically related words and phrases,
which are met in the same texts, should be enhanced: this action leads to their
larger contribution into topics found in these texts. We have conducted
experiments with several thesauri and found that for improving topic models, it
is useful to utilize domain-specific knowledge. If a general thesaurus, such as
WordNet, is used, the thesaurus-based improvement of topic models can be
achieved with excluding hyponymy relations in combined topic models.Comment: Accepted to AIST-2017 conference (http://aistconf.ru/). The final
publication will be available at link.springer.co
What Drives the International Development Agenda? An NLP Analysis of the United Nations General Debate 1970-2016
There is surprisingly little known about agenda setting for international
development in the United Nations (UN) despite it having a significant
influence on the process and outcomes of development efforts. This paper
addresses this shortcoming using a novel approach that applies natural language
processing techniques to countries' annual statements in the UN General Debate.
Every year UN member states deliver statements during the General Debate on
their governments' perspective on major issues in world politics. These
speeches provide invaluable information on state preferences on a wide range of
issues, including international development, but have largely been overlooked
in the study of global politics. This paper identifies the main international
development topics that states raise in these speeches between 1970 and 2016,
and examine the country-specific drivers of international development rhetoric
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