1,133 research outputs found
Handling Homographs in Neural Machine Translation
Homographs, words with different meanings but the same surface form, have
long caused difficulty for machine translation systems, as it is difficult to
select the correct translation based on the context. However, with the advent
of neural machine translation (NMT) systems, which can theoretically take into
account global sentential context, one may hypothesize that this problem has
been alleviated. In this paper, we first provide empirical evidence that
existing NMT systems in fact still have significant problems in properly
translating ambiguous words. We then proceed to describe methods, inspired by
the word sense disambiguation literature, that model the context of the input
word with context-aware word embeddings that help to differentiate the word
sense be- fore feeding it into the encoder. Experiments on three language pairs
demonstrate that such models improve the performance of NMT systems both in
terms of BLEU score and in the accuracy of translating homographs.Comment: NAACL201
Learning Character-level Compositionality with Visual Features
Previous work has modeled the compositionality of words by creating
character-level models of meaning, reducing problems of sparsity for rare
words. However, in many writing systems compositionality has an effect even on
the character-level: the meaning of a character is derived by the sum of its
parts. In this paper, we model this effect by creating embeddings for
characters based on their visual characteristics, creating an image for the
character and running it through a convolutional neural network to produce a
visual character embedding. Experiments on a text classification task
demonstrate that such model allows for better processing of instances with rare
characters in languages such as Chinese, Japanese, and Korean. Additionally,
qualitative analyses demonstrate that our proposed model learns to focus on the
parts of characters that carry semantic content, resulting in embeddings that
are coherent in visual space.Comment: Accepted to ACL 201
Periodic Cyclin-Cdk Activity Entrains an Autonomous Cdc14 Release Oscillator
SummaryOne oscillation of Cyclin-dependent kinase (Cdk) activity, largely driven by periodic synthesis and destruction of cyclins, is tightly coupled to a single complete eukaryotic cell division cycle. Tight linkage of different steps in diverse cell-cycle processes to Cdk activity has been proposed to explain this coupling. Here, we demonstrate an intrinsically oscillatory module controlling nucleolar release and resequestration of the Cdc14 phosphatase, which is essential for mitotic exit in budding yeast. We find that this Cdc14 release oscillator functions at constant and physiological cyclin-Cdk levels, and is therefore independent of the Cdk oscillator. However, the frequency of the release oscillator is regulated by cyclin-Cdk activity. This observation together with its mechanism suggests that the intrinsically autonomous Cdc14 release cycles are locked at once-per-cell-cycle through entrainment by the Cdk oscillator in wild-type cells. This concept may have broad implications for the structure and evolution of eukaryotic cell-cycle control
Generative Entity-to-Entity Stance Detection with Knowledge Graph Augmentation
Stance detection is typically framed as predicting the sentiment in a given
text towards a target entity. However, this setup overlooks the importance of
the source entity, i.e., who is expressing the opinion. In this paper, we
emphasize the need for studying interactions among entities when inferring
stances. We first introduce a new task, entity-to-entity (E2E) stance
detection, which primes models to identify entities in their canonical names
and discern stances jointly. To support this study, we curate a new dataset
with 10,619 annotations labeled at the sentence-level from news articles of
different ideological leanings. We present a novel generative framework to
allow the generation of canonical names for entities as well as stances among
them. We further enhance the model with a graph encoder to summarize entity
activities and external knowledge surrounding the entities. Experiments show
that our model outperforms strong comparisons by large margins. Further
analyses demonstrate the usefulness of E2E stance detection for understanding
media quotation and stance landscape, as well as inferring entity ideology.Comment: EMNLP'22 Main Conferenc
Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis
Prior work on ideology prediction has largely focused on single modalities,
i.e., text or images. In this work, we introduce the task of multimodal
ideology prediction, where a model predicts binary or five-point scale
ideological leanings, given a text-image pair with political content. We first
collect five new large-scale datasets with English documents and images along
with their ideological leanings, covering news articles from a wide range of US
mainstream media and social media posts from Reddit and Twitter. We conduct
in-depth analyses of news articles and reveal differences in image content and
usage across the political spectrum. Furthermore, we perform extensive
experiments and ablation studies, demonstrating the effectiveness of targeted
pretraining objectives on different model components. Our best-performing
model, a late-fusion architecture pretrained with a triplet objective over
multimodal content, outperforms the state-of-the-art text-only model by almost
4% and a strong multimodal baseline with no pretraining by over 3%.Comment: EMNLP 202
Development of a Test Facility for Air Revitalization Technology Evaluation
Development of new air revitalization system (ARS) technology can initially be performed in a subscale laboratory environment, but in order to advance the maturity level, the technology must be tested in an end-to-end integrated environment. The Air Revitalization Technology Evaluation Facility (ARTEF) at the NASA Johnson Space Center serves as a ground test bed for evaluating emerging ARS technologies in an environment representative of spacecraft atmospheres. At the center of the ARTEF is a hypobaric chamber which serves as a sealed atmospheric chamber for closed loop testing. A Human Metabolic Simulator (HMS) was custom-built to simulate the consumption of oxygen, and production of carbon dioxide, moisture and heat of up to eight persons. A multitude of gas analyzers and dew point sensors are used to monitor the chamber atmosphere upstream and downstream of a test article. A robust vacuum system is needed to simulate the vacuum of space. A reliable data acquisition and control system is required to connect all the subsystems together. This paper presents the capabilities of the integrated test facility and some of the issues encountered during the integration
Crossing the Aisle: Unveiling Partisan and Counter-Partisan Events in News Reporting
News media is expected to uphold unbiased reporting. Yet they may still
affect public opinion by selectively including or omitting events that support
or contradict their ideological positions. Prior work in NLP has only studied
media bias via linguistic style and word usage. In this paper, we study to
which degree media balances news reporting and affects consumers through event
inclusion or omission. We first introduce the task of detecting both partisan
and counter-partisan events: events that support or oppose the author's
political ideology. To conduct our study, we annotate a high-quality dataset,
PAC, containing 8,511 (counter-)partisan event annotations in 304 news articles
from ideologically diverse media outlets. We benchmark PAC to highlight the
challenges of this task. Our findings highlight both the ways in which the news
subtly shapes opinion and the need for large language models that better
understand events within a broader context. Our dataset can be found at
https://github.com/launchnlp/Partisan-Event-Dataset.Comment: EMNLP'23 Finding
You Are What You Annotate: Towards Better Models through Annotator Representations
Annotator disagreement is ubiquitous in natural language processing (NLP)
tasks. There are multiple reasons for such disagreements, including the
subjectivity of the task, difficult cases, unclear guidelines, and so on.
Rather than simply aggregating labels to obtain data annotations, we instead
try to directly model the diverse perspectives of the annotators, and
explicitly account for annotators' idiosyncrasies in the modeling process by
creating representations for each annotator (annotator embeddings) and also
their annotations (annotation embeddings). In addition, we propose TID-8, The
Inherent Disagreement - 8 dataset, a benchmark that consists of eight existing
language understanding datasets that have inherent annotator disagreement. We
test our approach on TID-8 and show that our approach helps models learn
significantly better from disagreements on six different datasets in TID-8
while increasing model size by fewer than 1% parameters. By capturing the
unique tendencies and subjectivity of individual annotators through embeddings,
our representations prime AI models to be inclusive of diverse viewpoints.Comment: Accepted to Findings of EMNLP 202
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