The kNN-CTC model has proven to be effective for monolingual automatic speech
recognition (ASR). However, its direct application to multilingual scenarios
like code-switching, presents challenges. Although there is potential for
performance improvement, a kNN-CTC model utilizing a single bilingual datastore
can inadvertently introduce undesirable noise from the alternative language. To
address this, we propose a novel kNN-CTC-based code-switching ASR (CS-ASR)
framework that employs dual monolingual datastores and a gated datastore
selection mechanism to reduce noise interference. Our method selects the
appropriate datastore for decoding each frame, ensuring the injection of
language-specific information into the ASR process. We apply this framework to
cutting-edge CTC-based models, developing an advanced CS-ASR system. Extensive
experiments demonstrate the remarkable effectiveness of our gated datastore
mechanism in enhancing the performance of zero-shot Chinese-English CS-ASR.

我们提出了一种新颖的 kNN-CTC 基于代码切换 ASR 框架，它采用双语言数据存储和门控数据存储选择机制以减少噪音干扰。我们将此框架应用于先进的 CTC-based 模型，开发出一种先进的中英文代码切换 ASR 系统。广泛的实验证明了我们的门控数据存储机制在提高零 - shot 中英文代码切换 ASR 性能方面的卓越效果。

利用 kNN-CTC 和门控单语数据存储改进零 - shot 中英混编自动语音识别

Improving Zero-Shot Chinese-English Code-Switching ASR with kNN-CTC and  Gated Monolingual Datastores

Factual consistency evaluation is often conducted using Natural Language
Inference (NLI) models, yet these models exhibit limited success in evaluating
summaries. Previous work improved such models with synthetic training data.
However, the data is typically based on perturbed human-written summaries,
which often differ in their characteristics from real model-generated summaries
and have limited coverage of possible factual errors. Alternatively, large
language models (LLMs) have recently shown promising results in directly
evaluating generative tasks, but are too computationally expensive for
practical use. Motivated by these limitations, we introduce TrueTeacher, a
method for generating synthetic data by annotating diverse model-generated
summaries using a LLM. Unlike prior work, TrueTeacher does not rely on
human-written summaries, and is multilingual by nature. Experiments on the TRUE
benchmark show that a student model trained using our data, substantially
outperforms both the state-of-the-art model with similar capacity, and the LLM
teacher. In a systematic study, we compare TrueTeacher to existing synthetic
data generation methods and demonstrate its superiority and robustness to
domain-shift. Using the the mFACE dataset, we also show that our method
generalizes to multilingual scenarios. Finally, we release a large-scale
synthetic dataset with 1.4M examples generated using TrueTeacher.

TrueTeacher 通过用 LLM 注释各种模型生成的摘要来生成合成数据的方法，优于现有的合成数据生成方法，并可在多语言情况下通用。

TrueTeacher：利用大型语言模型学习事实一致性评估

TrueTeacher: Learning Factual Consistency Evaluation with Large Language  Models

Topic segmentation is critical in key NLP tasks and recent works favor highly
effective neural supervised approaches. However, current neural solutions are
arguably limited in how they model context. In this paper, we enhance a
segmenter based on a hierarchical attention BiLSTM network to better model
context, by adding a coherence-related auxiliary task and restricted
self-attention. Our optimized segmenter outperforms SOTA approaches when
trained and tested on three datasets. We also the robustness of our proposed
model in domain transfer setting by training a model on a large-scale dataset
and testing it on four challenging real-world benchmarks. Furthermore, we apply
our proposed strategy to two other languages (German and Chinese), and show its
effectiveness in multilingual scenarios.

本文提出了一个基于层级注意力双向 LSTM 网络的主题分割器，通过添加相干性相关的辅助任务和受限制自注意力来更好地建模上下文，从而超过了 SOTA 方法，并在领域转移设置中证明了其鲁棒性和在多语言场景中的有效性。

神经主题分割中上下文建模的改进

Improving Context Modeling in Neural Topic Segmentation

Lexicon-based methods using syntactic rules for polarity classification rely
on parsers that are dependent on the language and on treebank guidelines. Thus,
rules are also dependent and require adaptation, especially in multilingual
scenarios. We tackle this challenge in the context of the Iberian Peninsula,
releasing the first symbolic syntax-based Iberian system with rules shared
across five official languages: Basque, Catalan, Galician, Portuguese and
Spanish. The model is made available.

本文中提出了一种基于符号语法的伊比利亚系统，使用词典和句法规则对多种语言的情感分类进行了改进。