4,246 research outputs found
Cooperative Learning of Zero-Shot Machine Reading Comprehension
Pretrained language models have significantly improved the performance of
down-stream language understanding tasks, including extractive question
answering, by providing high-quality contextualized word embeddings. However,
learning question answering models still need large-scaled data annotation in
specific domains. In this work, we propose a cooperative, self-play learning
framework, REGEX, for question generation and answering. REGEX is built upon a
masked answer extraction task with an interactive learning environment
containing an answer entity REcognizer, a question Generator, and an answer
EXtractor. Given a passage with a masked entity, the generator generates a
question around the entity, and the extractor is trained to extract the masked
entity with the generated question and raw texts. The framework allows the
training of question generation and answering models on any text corpora
without annotation. We further leverage a reinforcement learning technique to
reward generating high-quality questions and to improve the answer extraction
model's performance. Experiment results show that REGEX outperforms the
state-of-the-art (SOTA) pretrained language models and zero-shot approaches on
standard question-answering benchmarks, and yields the new SOTA performance
under the zero-shot setting
Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Mode
In this paper, we show that representations capturing syllabic units emerge
when training a self-supervised speech model with a visually-grounded training
objective. We demonstrate that a nearly identical model architecture (HuBERT)
trained with a masked language modeling loss does not exhibit this same
ability, suggesting that the visual grounding objective is responsible for the
emergence of this phenomenon. We propose the use of a minimum cut algorithm to
automatically predict syllable boundaries in speech, followed by a 2-stage
clustering method to group identical syllables together. We show that our model
not only outperforms a state-of-the-art syllabic segmentation method on the
language it was trained on (English), but also generalizes in a zero-shot
fashion to Estonian. Finally, we show that the same model is capable of
zero-shot generalization for a word segmentation task on 4 other languages from
the Zerospeech Challenge, in some cases beating the previous state-of-the-art.Comment: Interspeech 2023. Code & Model:
https://github.com/jasonppy/syllable-discover
Bis[(E)-4-bromo-2-(ethoxyiminomethyl)phenolato-κ2 N,O 1]copper(II)
The title compound, [Cu(C9H9BrNO2)2], is a centrosymmetric mononuclear copper(II) complex. The Cu atom is four-coordinated in a trans-CuN2O2 square-planar geometry by two phenolate O and two oxime N atoms from two symmetry-related N,O-bidentate (E)-4-bromo-2-(ethoxyiminomethyl)phenolate oxime-type ligands. An interesting feature of the crystal structure is the centrosymmetric intermolecular Cu⋯O interaction [3.382 (1) Å], which establishes an infinite chain structure along the b axis
Improving Textless Spoken Language Understanding with Discrete Units as Intermediate Target
Spoken Language Understanding (SLU) is a task that aims to extract semantic
information from spoken utterances. Previous research has made progress in
end-to-end SLU by using paired speech-text data, such as pre-trained Automatic
Speech Recognition (ASR) models or paired text as intermediate targets.
However, acquiring paired transcripts is expensive and impractical for
unwritten languages. On the other hand, Textless SLU extracts semantic
information from speech without utilizing paired transcripts. However, the
absence of intermediate targets and training guidance for textless SLU often
results in suboptimal performance. In this work, inspired by the
content-disentangled discrete units from self-supervised speech models, we
proposed to use discrete units as intermediate guidance to improve textless SLU
performance. Our method surpasses the baseline method on five SLU benchmark
corpora. Additionally, we find that unit guidance facilitates few-shot learning
and enhances the model's ability to handle noise.Comment: Accepted by interspeech 202
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