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