959 research outputs found
平易なコーパスを用いないテキスト平易化
首都大学東京, 2018-03-25, 博士(工学)首都大学東
Language-Independent Tokenisation Rivals Language-Specific Tokenisation for Word Similarity Prediction.
Language-independent tokenisation (LIT) methods that do not require labelled
language resources or lexicons have recently gained popularity because of their
applicability in resource-poor languages. Moreover, they compactly represent a
language using a fixed size vocabulary and can efficiently handle unseen or
rare words. On the other hand, language-specific tokenisation (LST) methods
have a long and established history, and are developed using carefully created
lexicons and training resources. Unlike subtokens produced by LIT methods, LST
methods produce valid morphological subwords. Despite the contrasting
trade-offs between LIT vs. LST methods, their performance on downstream NLP
tasks remain unclear. In this paper, we empirically compare the two approaches
using semantic similarity measurement as an evaluation task across a diverse
set of languages. Our experimental results covering eight languages show that
LST consistently outperforms LIT when the vocabulary size is large, but LIT can
produce comparable or better results than LST in many languages with
comparatively smaller (i.e. less than 100K words) vocabulary sizes, encouraging
the use of LIT when language-specific resources are unavailable, incomplete or
a smaller model is required. Moreover, we find that smoothed inverse frequency
(SIF) to be an accurate method to create word embeddings from subword
embeddings for multilingual semantic similarity prediction tasks. Further
analysis of the nearest neighbours of tokens show that semantically and
syntactically related tokens are closely embedded in subword embedding spacesComment: To appear in the 12th Language Resources and Evaluation (LREC 2020)
Conferenc
A survey on lexical simplification
Lexical Simplification is the process of replacing complex words in a given sentence with simpler alternatives of equivalent meaning. This task has wide applicability both as an assistive technology for readers with cognitive impairments or disabilities, such as Dyslexia and Aphasia, and as a pre-processing tool for other Natural Language Processing tasks, such as machine translation and summarisation. The problem is commonly framed as a pipeline of four steps: the identification of complex words, the generation of substitution candidates, the selection of those candidates that fit the context, and the ranking of the selected substitutes according to their simplicity. In this survey we review the literature for each step in this typical Lexical Simplification pipeline and provide a benchmarking of existing approaches for these steps on publicly available datasets. We also provide pointers for datasets and resources available for the task
RAMP: Retrieval and Attribute-Marking Enhanced Prompting for Attribute-Controlled Translation
Attribute-controlled translation (ACT) is a subtask of machine translation that involves controlling stylistic or linguistic attributes (like formality and gender) of translation outputs. While ACT has garnered attention in recent years due to its usefulness in real-world applications, progress in the task is currently limited by dataset availability, since most prior approaches rely on supervised methods. To address this limitation, we propose Retrieval and Attribute-Marking enhanced Prompting (RAMP), which leverages large multilingual language models to perform ACT in few-shot and zero-shot settings. RAMP improves generation accuracy over the standard prompting approach by (1) incorporating a semantic similarity retrieval component for selecting similar in-context examples, and (2) marking in-context examples with attribute annotations. Our comprehensive experiments show that RAMP is a viable approach in both zero-shot and few-shot settings
Mediated discourse at the European Parliament: Empirical investigations
The purpose of this book is to showcase a diverse set of directions in empirical research on mediated discourse, reflecting on the state-of-the-art and the increasing intersection between Corpus-based Interpreting Studies (CBIS) and Corpus-based Translation Studies (CBTS). Undeniably, data from the European Parliament (EP) offer a great opportunity for such research. Not only does the institution provide a sizeable sample of oral debates held at the EP together with their simultaneous interpretations into all languages of the European Union. It also makes available written verbatim reports of the original speeches, which used to be translated. From a methodological perspective, EP materials thus guarantee a great degree of homogeneity, which is particularly valuable in corpus studies, where data comparability is frequently a challenge.
In this volume, progress is visible in both CBIS and CBTS. In interpreting, it manifests itself notably in the availability of comprehensive transcription, annotation and alignment systems. In translation, datasets are becoming substantially richer in metadata, which allow for increasingly refined multi-factorial analysis. At the crossroads between the two fields, intermodal investigations bring to the fore what these mediation modes have in common and how they differ. The volume is thus aimed in particular at Interpreting and Translation scholars looking for new descriptive insights and methodological approaches in the investigation of mediated discourse, but it may be also of interest for (corpus) linguists analysing parliamentary discourse in general
Empirical investigations
The purpose of this book is to showcase a diverse set of directions in empirical research on mediated discourse, reflecting on the state-of-the-art and the increasing intersection between Corpus-based Interpreting Studies (CBIS) and Corpus-based Translation Studies (CBTS). Undeniably, data from the European Parliament (EP) offer a great opportunity for such research. Not only does the institution provide a sizeable sample of oral debates held at the EP together with their simultaneous interpretations into all languages of the European Union. It also makes available written verbatim reports of the original speeches, which used to be translated. From a methodological perspective, EP materials thus guarantee a great degree of homogeneity, which is particularly valuable in corpus studies, where data comparability is frequently a challenge.
In this volume, progress is visible in both CBIS and CBTS. In interpreting, it manifests itself notably in the availability of comprehensive transcription, annotation and alignment systems. In translation, datasets are becoming substantially richer in metadata, which allow for increasingly refined multi-factorial analysis. At the crossroads between the two fields, intermodal investigations bring to the fore what these mediation modes have in common and how they differ. The volume is thus aimed in particular at Interpreting and Translation scholars looking for new descriptive insights and methodological approaches in the investigation of mediated discourse, but it may be also of interest for (corpus) linguists analysing parliamentary discourse in general
Re-examining Phonological and Lexical Correlates of Second Language Comprehensibility:The Role of Rater Experience
Few researchers and teachers would disagree that some linguistic aspects
of second language (L2) speech are more crucial than others for successful
communication. Underlying this idea is the assumption that communicative
success can be broadly defined in terms of speakers’ ability to convey the
intended meaning to the interlocutor, which is frequently captured through
a listener-based rating of comprehensibility or ease of understanding (e.g.
Derwing & Munro, 2009; Levis, 2005). Previous research has shown that
communicative success – for example, as defined through comprehensible L2
speech – depends on several linguistic dimensions of L2 output, including its
segmental and suprasegmental pronunciation, fluency-based characteristics,
lexical and grammatical content, as well as discourse structure (e.g. Field,
2005; Hahn, 2004; Kang et al., 2010; Trofimovich & Isaacs, 2012). Our chief
objective in the current study was to explore the L2 comprehensibility construct from a language assessment perspective (e.g. Isaacs & Thomson, 2013),
by targeting rater experience as a possible source of variance influencing the
degree to which raters use various characteristics of speech in judging L2
comprehensibility. In keeping with this objective, we asked the following
question: What is the extent to which linguistic aspects of L2 speech contributing to comprehensibility ratings depend on raters’ experience
Automatic Image Captioning with Style
This thesis connects two core topics in machine learning, vision
and language. The problem of choice is image caption generation:
automatically constructing natural language descriptions of image
content. Previous research into image caption generation has
focused on generating purely descriptive captions; I focus on
generating visually relevant captions with a distinct linguistic
style. Captions with style have the potential to ease
communication and add a new layer of personalisation.
First, I consider naming variations in image captions, and
propose a method for predicting context-dependent names that
takes into account visual and linguistic information. This method
makes use of a large-scale image caption dataset, which I also
use to explore naming conventions and report naming conventions
for hundreds of animal classes. Next I propose the SentiCap
model, which relies on recent advances in artificial neural
networks to generate visually relevant image captions with
positive or negative sentiment. To balance descriptiveness and
sentiment, the SentiCap model dynamically switches between two
recurrent neural networks, one tuned for descriptive words and
one for sentiment words. As the first published model for
generating captions with sentiment, SentiCap has influenced a
number of subsequent works. I then investigate the sub-task of
modelling styled sentences without images. The specific task
chosen is sentence simplification: rewriting news article
sentences to make them easier to understand.
For this task I design a neural sequence-to-sequence model that
can work with
limited training data, using novel adaptations for word copying
and sharing
word embeddings. Finally, I present SemStyle, a system for
generating visually
relevant image captions in the style of an arbitrary text corpus.
A shared term
space allows a neural network for vision and content planning to
communicate
with a network for styled language generation. SemStyle achieves
competitive
results in human and automatic evaluations of descriptiveness and
style.
As a whole, this thesis presents two complete systems for styled
caption generation that are first of their kind and demonstrate,
for the first time, that automatic style transfer for image
captions is achievable. Contributions also include novel ideas
for object naming and sentence simplification. This thesis opens
up inquiries into highly personalised image captions; large scale
visually grounded concept naming; and more generally, styled text
generation with content control
An Automatic Modern Standard Arabic Text Simplification System: A Corpus-Based Approach
This thesis brings together an overview of Text Readability (TR) about Text Simplification (TS) with an application of both to Modern Standard Arabic (MSA). It will present our findings on using automatic TR and TS tools to teach MSA, along with challenges, limitations, and recommendations about enhancing the TR and TS models.
Reading is one of the most vital tasks that provide language input for communication and comprehension skills. It is proved that the use of long sentences, connected sentences, embedded phrases, passive voices, non- standard word orders, and infrequent words can increase the text difficulty for people with low literacy levels, as well as second language learners. The thesis compares the use of sentence embeddings of different types (fastText, mBERT, XLM-R and Arabic-BERT), as well as traditional language features such as POS tags, dependency trees, readability scores and frequency lists for language learners. The accuracy of the 3-way CEFR (The Common European Framework of Reference for Languages Proficiency Levels) classification is F-1 of 0.80 and 0.75 for Arabic-Bert and XLM-R classification, respectively and 0.71 Spearman correlation for the regression task. At the same time, the binary difficulty classifier reaches F-1 0.94 and F-1 0.98 for the sentence-pair semantic similarity classifier.
TS is an NLP task aiming to reduce the linguistic complexity of the text while maintaining its meaning and original information (Siddharthan, 2002; Camacho Collados, 2013; Saggion, 2017). The simplification study experimented using two approaches: (i) a classification approach and (ii) a generative approach. It then evaluated the effectiveness of these methods using the BERTScore (Zhang et al., 2020) evaluation metric. The simple sentences produced by the mT5 model achieved P 0.72, R 0.68 and F-1 0.70 via BERTScore while combining Arabic- BERT and fastText achieved P 0.97, R 0.97 and F-1 0.97.
To reiterate, this research demonstrated the effectiveness of the implementation of a corpus-based method combined with extracting extensive linguistic features via the latest NLP techniques. It provided insights which can be of use in various Arabic corpus studies and NLP tasks such as translation for educational purposes
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