5 research outputs found
Towards Controlled Transformation of Sentiment in Sentences
An obstacle to the development of many natural language processing products
is the vast amount of training examples necessary to get satisfactory results.
The generation of these examples is often a tedious and time-consuming task.
This paper this paper proposes a method to transform the sentiment of sentences
in order to limit the work necessary to generate more training data. This means
that one sentence can be transformed to an opposite sentiment sentence and
should reduce by half the work required in the generation of text. The proposed
pipeline consists of a sentiment classifier with an attention mechanism to
highlight the short phrases that determine the sentiment of a sentence. Then,
these phrases are changed to phrases of the opposite sentiment using a baseline
model and an autoencoder approach. Experiments are run on both the separate
parts of the pipeline as well as on the end-to-end model. The sentiment
classifier is tested on its accuracy and is found to perform adequately. The
autoencoder is tested on how well it is able to change the sentiment of an
encoded phrase and it was found that such a task is possible. We use human
evaluation to judge the performance of the full (end-to-end) pipeline and that
reveals that a model using word vectors outperforms the encoder model.
Numerical evaluation shows that a success rate of 54.7% is achieved on the
sentiment change.Comment: Accepted at ICAART 2019, 8 page
Towards Controlled Transformation of Sentiment in Sentences
An obstacle to the development of many natural language processing products
is the vast amount of training examples necessary to get satisfactory results.
The generation of these examples is often a tedious and time-consuming task.
This paper this paper proposes a method to transform the sentiment of sentences
in order to limit the work necessary to generate more training data. This means
that one sentence can be transformed to an opposite sentiment sentence and
should reduce by half the work required in the generation of text. The proposed
pipeline consists of a sentiment classifier with an attention mechanism to
highlight the short phrases that determine the sentiment of a sentence. Then,
these phrases are changed to phrases of the opposite sentiment using a baseline
model and an autoencoder approach. Experiments are run on both the separate
parts of the pipeline as well as on the end-to-end model. The sentiment
classifier is tested on its accuracy and is found to perform adequately. The
autoencoder is tested on how well it is able to change the sentiment of an
encoded phrase and it was found that such a task is possible. We use human
evaluation to judge the performance of the full (end-to-end) pipeline and that
reveals that a model using word vectors outperforms the encoder model.
Numerical evaluation shows that a success rate of 54.7% is achieved on the
sentiment change.Comment: Accepted at ICAART 2019, 8 page
Automatically Neutralizing Subjective Bias in Text
Texts like news, encyclopedias, and some social media strive for objectivity.
Yet bias in the form of inappropriate subjectivity - introducing attitudes via
framing, presupposing truth, and casting doubt - remains ubiquitous. This kind
of bias erodes our collective trust and fuels social conflict. To address this
issue, we introduce a novel testbed for natural language generation:
automatically bringing inappropriately subjective text into a neutral point of
view ("neutralizing" biased text). We also offer the first parallel corpus of
biased language. The corpus contains 180,000 sentence pairs and originates from
Wikipedia edits that removed various framings, presuppositions, and attitudes
from biased sentences. Last, we propose two strong encoder-decoder baselines
for the task. A straightforward yet opaque CONCURRENT system uses a BERT
encoder to identify subjective words as part of the generation process. An
interpretable and controllable MODULAR algorithm separates these steps, using
(1) a BERT-based classifier to identify problematic words and (2) a novel join
embedding through which the classifier can edit the hidden states of the
encoder. Large-scale human evaluation across four domains (encyclopedias, news
headlines, books, and political speeches) suggests that these algorithms are a
first step towards the automatic identification and reduction of bias.Comment: To appear at AAAI 202
Text Style Transfer: A Review and Experimental Evaluation
The stylistic properties of text have intrigued computational linguistics
researchers in recent years. Specifically, researchers have investigated the
Text Style Transfer (TST) task, which aims to change the stylistic properties
of the text while retaining its style independent content. Over the last few
years, many novel TST algorithms have been developed, while the industry has
leveraged these algorithms to enable exciting TST applications. The field of
TST research has burgeoned because of this symbiosis. This article aims to
provide a comprehensive review of recent research efforts on text style
transfer. More concretely, we create a taxonomy to organize the TST models and
provide a comprehensive summary of the state of the art. We review the existing
evaluation methodologies for TST tasks and conduct a large-scale
reproducibility study where we experimentally benchmark 19 state-of-the-art TST
algorithms on two publicly available datasets. Finally, we expand on current
trends and provide new perspectives on the new and exciting developments in the
TST field