326 research outputs found
A User-Centered Evaluation of Spanish Text Simplification
We present an evaluation of text simplification (TS) in Spanish for a
production system, by means of two corpora focused in both complex-sentence and
complex-word identification. We compare the most prevalent Spanish-specific
readability scores with neural networks, and show that the latter are
consistently better at predicting user preferences regarding TS. As part of our
analysis, we find that multilingual models underperform against equivalent
Spanish-only models on the same task, yet all models focus too often on
spurious statistical features, such as sentence length. We release the corpora
in our evaluation to the broader community with the hopes of pushing forward
the state-of-the-art in Spanish natural language processing.Comment: Data at https://github.com/microsoft/BrevE-CLar
EMMA-X: An EM-like Multilingual Pre-training Algorithm for Cross-lingual Representation Learning
Expressing universal semantics common to all languages is helpful in
understanding the meanings of complex and culture-specific sentences. The
research theme underlying this scenario focuses on learning universal
representations across languages with the usage of massive parallel corpora.
However, due to the sparsity and scarcity of parallel data, there is still a
big challenge in learning authentic ``universals'' for any two languages. In
this paper, we propose EMMA-X: an EM-like Multilingual pre-training Algorithm,
to learn (X)Cross-lingual universals with the aid of excessive multilingual
non-parallel data. EMMA-X unifies the cross-lingual representation learning
task and an extra semantic relation prediction task within an EM framework.
Both the extra semantic classifier and the cross-lingual sentence encoder
approximate the semantic relation of two sentences, and supervise each other
until convergence. To evaluate EMMA-X, we conduct experiments on XRETE, a newly
introduced benchmark containing 12 widely studied cross-lingual tasks that
fully depend on sentence-level representations. Results reveal that EMMA-X
achieves state-of-the-art performance. Further geometric analysis of the built
representation space with three requirements demonstrates the superiority of
EMMA-X over advanced models.Comment: Accepted by NeurIPS 202
Evaluating Feature-Specific Similarity Metrics using Human Judgments for Norwegian News
Masteroppgave i informasjonsvitenskapINFO390MASV-INF
Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology
Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
Mind the source data! : Translation equivalents and translation stimuli from parallel corpora
Statements like ‘Word X of language A is translated with word Y of language B’ are incorrect, although they are quite common: words cannot be translated, as translation takes place on the level of sentences or higher. A better term for the correspondence between lexical items of source texts and their matches in target texts would be translation equivalence (Teq). In addition to Teq, there exists a reverse relation—translation stimulation (Tst), which is a correspondence between the lexical items of target texts and their matches (=stimuli) in source texts. Translation equivalents and translation stimuli must be studied separately and based on natural direct translations. It is not advisable to use pseudo-parallel texts, i.e. aligned pairs of translations from a ‘hub’ language, because such data do not reflect real translation processes. Both Teq and Tst are lexical functions, and they are not applicable to function words like prepositions, conjunctions, or particles, although it is technically possible to find Teq and Tst candidates for such words as well. The process of choosing function words when translating does not proceed in the same way as choosing lexical units: first, a relevant construction is chosen, and next, it is filled with relevant function words. In this chapter, the difference between Teq and Tst will be shown in examples from Russian–Finnish and Finnish–Russian parallel corpora. The use of Teq and Tst for translation studies and contrastive semantic research will be discussed, along with the importance of paying attention to the nature of the texts when analysing corpus findings.acceptedVersionPeer reviewe
VIBE: Topic-Driven Temporal Adaptation for Twitter Classification
Language features are evolving in real-world social media, resulting in the
deteriorating performance of text classification in dynamics. To address this
challenge, we study temporal adaptation, where models trained on past data are
tested in the future. Most prior work focused on continued pretraining or
knowledge updating, which may compromise their performance on noisy social
media data. To tackle this issue, we reflect feature change via modeling latent
topic evolution and propose a novel model, VIBE: Variational Information
Bottleneck for Evolutions. Concretely, we first employ two Information
Bottleneck (IB) regularizers to distinguish past and future topics. Then, the
distinguished topics work as adaptive features via multi-task training with
timestamp and class label prediction. In adaptive learning, VIBE utilizes
retrieved unlabeled data from online streams created posterior to training data
time. Substantial Twitter experiments on three classification tasks show that
our model, with only 3% of data, significantly outperforms previous
state-of-the-art continued-pretraining methods.Comment: accepted by EMNLP 202
SeamlessM4T-Massively Multilingual & Multimodal Machine Translation
What does it take to create the Babel Fish, a tool that can help individuals
translate speech between any two languages? While recent breakthroughs in
text-based models have pushed machine translation coverage beyond 200
languages, unified speech-to-speech translation models have yet to achieve
similar strides. More specifically, conventional speech-to-speech translation
systems rely on cascaded systems that perform translation progressively,
putting high-performing unified systems out of reach. To address these gaps, we
introduce SeamlessM4T, a single model that supports speech-to-speech
translation, speech-to-text translation, text-to-speech translation,
text-to-text translation, and automatic speech recognition for up to 100
languages. To build this, we used 1 million hours of open speech audio data to
learn self-supervised speech representations with w2v-BERT 2.0. Subsequently,
we created a multimodal corpus of automatically aligned speech translations.
Filtered and combined with human-labeled and pseudo-labeled data, we developed
the first multilingual system capable of translating from and into English for
both speech and text. On FLEURS, SeamlessM4T sets a new standard for
translations into multiple target languages, achieving an improvement of 20%
BLEU over the previous SOTA in direct speech-to-text translation. Compared to
strong cascaded models, SeamlessM4T improves the quality of into-English
translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in
speech-to-speech. Tested for robustness, our system performs better against
background noises and speaker variations in speech-to-text tasks compared to
the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and
added toxicity to assess translation safety. Finally, all contributions in this
work are open-sourced and accessible at
https://github.com/facebookresearch/seamless_communicatio
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