Spoken language understanding (SLU) tasks have been studied for many decades in the speech research community, but have not received as much attention as lower-level tasks like speech and speaker recognition. In particular, there are not nearly as many SLU task benchmarks, and many of the existing ones use data that is not freely available to all researchers. Recent work has begun to introduce such benchmark datasets for several tasks. In this work, we introduce several new annotated SLU benchmark tasks based on freely available speech data, which complement existing benchmarks and address gaps in the SLU evaluation landscape. We contribute four tasks: question answering and summarization involve inference over longer speech sequences; named entity localization addresses the speech-specific task of locating the targeted content in the signal; dialog act classification identifies the function of a given speech utterance. We follow the blueprint of the Spoken Language Understanding Evaluation (SLUE) benchmark suite. In order to facilitate the development of SLU models that leverage the success of pre-trained speech representations, we will be publishing for each task (i) annotations for a relatively small fine-tuning set, (ii) annotated development and test sets, and (iii) baseline models for easy reproducibility and comparisons. In this work, we present the details of data collection and annotation and the performance of the baseline models. We also perform sensitivity analysis of pipeline models' performance (speech recognizer + text model) to the speech recognition accuracy, using more than 20 state-of-the-art speech recognition models.

通过共享数据集和基准，已经促进了语音处理的进展。历史上，这些都集中在自动语音识别（ASR），扬声器标识或其他较低级别的任务上。兴趣在更高层次的口语中越来越多，理解任务，包括使用端到端模型，但是此类任务的注释数据集较少。与此同时，最近的工作显示了预先培训通用表示的可能性，然后使用相对较少标记的数据进行微调的多个任务。我们建议为口语语言理解（屠宰）创建一套基准任务，由有限尺寸标记的培训集和相应的评估集组成。该资源将允许研究界跟踪进度，评估高级任务的预先接受预期的表示，并研究开放的问题，例如管道与端到端方法的实用性。我们介绍了雪橇基准套件的第一阶段，包括指定实体识别，情感分析和相应数据集上的ASR。我们专注于自然产生的（未读取或综合）语音和自由可用的数据集。我们为VoxceReb和Voxpopuli数据集的子集提供新的转录和注释，基线模型的评估指标和结果，以及重现基线的开源工具包，并评估新模型。

口语语言理解（SLU）任务涉及从语音音频信号映射到语义标签。鉴于此类任务的复杂性，可能预期良好的性能需要大量标记的数据集，这很难为每个新任务和域收集。但是，最近的自我监督讲话表现的进步使得考虑使用有限标记的数据学习SLU模型是可行的。在这项工作中，我们专注于低资源讨论（ner）并解决问题：超越自我监督的预培训，我们如何使用未为任务注释的外部语音和/或文本数据？我们借鉴了各种方法，包括自我训练，知识蒸馏和转移学习，并考虑其对端到端模型和管道（语音识别后跟文本型号）的适用性。我们发现，这些方法中的几种方法可以在资源受限的环境中提高绩效，超出了训练有素的表示的福利。与事先工作相比，我们发现改进的F1分数高达16％。虽然最好的基线模型是一种管道方法，但使用外部数据时最终通过端到端模型实现的最佳性能。我们提供了详细的比较和分析，例如，端到端模型能够专注于更加立列人的单词。

Collecting sufficient labeled data for spoken language understanding (SLU) is expensive and time-consuming. Recent studies achieved promising results by using pre-trained models in low-resource scenarios. Inspired by this, we aim to ask: which (if any) pre-training strategies can improve performance across SLU benchmarks? To answer this question, we employ four types of pre-trained models and their combinations for SLU. We leverage self-supervised speech and language models (LM) pre-trained on large quantities of unpaired data to extract strong speech and text representations. We also explore using supervised models pre-trained on larger external automatic speech recognition (ASR) or SLU corpora. We conduct extensive experiments on the SLU Evaluation (SLUE) benchmark and observe self-supervised pre-trained models to be more powerful, with pre-trained LM and speech models being most beneficial for the Sentiment Analysis and Named Entity Recognition task, respectively.

口头问题答案（SQA）是要从一个问题中找到口语文件的答案，这对于个人助理回复用户的查询至关重要。现有的SQA方法均取决于自动语音识别（ASR）成绩单。不仅需要对ASR进行大量的注释数据，这些数据是时间且成本良好的低资源语言的收集，而且更重要的是，问题的答案通常包括名称实体或不可能的唱片词正确识别。此外，ASR旨在最大程度地减少所有单词的识别错误，包括与SQA任务无关的许多函数单词。因此，尽管非常困难，但始终是高度期望的无ASR转录本（无文本）的SQA。这项工作提出了离散的口语自适应学习（双重），利用未标记的数据进行预训练，并通过SQA下游任务进行了微调。口语答案的时间间隔可以直接从口语文件预测。我们还发布了一个新的SQA基准语料库NMSQA，以了解具有更现实的方案的数据。我们从经验上表明，双重收益结果与通过级联ASR和文本质量质量质量质量质量质量质量质量质量质量质量质量质量质量质量数据相媲美，并与现实世界中的数据相当。我们的代码和模型将是开源的。

Personal assistants, automatic speech recognizers and dialogue understanding systems are becoming more critical in our interconnected digital world. A clear example is air traffic control (ATC) communications. ATC aims at guiding aircraft and controlling the airspace in a safe and optimal manner. These voice-based dialogues are carried between an air traffic controller (ATCO) and pilots via very-high frequency radio channels. In order to incorporate these novel technologies into ATC (low-resource domain), large-scale annotated datasets are required to develop the data-driven AI systems. Two examples are automatic speech recognition (ASR) and natural language understanding (NLU). In this paper, we introduce the ATCO2 corpus, a dataset that aims at fostering research on the challenging ATC field, which has lagged behind due to lack of annotated data. The ATCO2 corpus covers 1) data collection and pre-processing, 2) pseudo-annotations of speech data, and 3) extraction of ATC-related named entities. The ATCO2 corpus is split into three subsets. 1) ATCO2-test-set corpus contains 4 hours of ATC speech with manual transcripts and a subset with gold annotations for named-entity recognition (callsign, command, value). 2) The ATCO2-PL-set corpus consists of 5281 hours of unlabeled ATC data enriched with automatic transcripts from an in-domain speech recognizer, contextual information, speaker turn information, signal-to-noise ratio estimate and English language detection score per sample. Both available for purchase through ELDA at http://catalog.elra.info/en-us/repository/browse/ELRA-S0484. 3) The ATCO2-test-set-1h corpus is a one-hour subset from the original test set corpus, that we are offering for free at https://www.atco2.org/data. We expect the ATCO2 corpus will foster research on robust ASR and NLU not only in the field of ATC communications but also in the general research community.

自动语音识别和文本到语音系统主要以监督方式培训，需要高质量，准确标记的语音数据集。在这项工作中，我们研究语音数据的常见问题，并为语音数据集的构建和交互式错误分析引入工具箱。施工工具基于K \“urzinger等。工作，并且，尽我们所知，数据集探索工具是世界上第一个这类开源工具。我们演示了如何应用这些工具来创建一个俄语语音数据集并分析现有语音数据集（多语种LibrisPeech，Mozilla Common语音）。该工具是开放的，作为Nemo框架的一部分。

We present NusaCrowd, a collaborative initiative to collect and unite existing resources for Indonesian languages, including opening access to previously non-public resources. Through this initiative, we have has brought together 137 datasets and 117 standardized data loaders. The quality of the datasets has been assessed manually and automatically, and their effectiveness has been demonstrated in multiple experiments. NusaCrowd's data collection enables the creation of the first zero-shot benchmarks for natural language understanding and generation in Indonesian and its local languages. Furthermore, NusaCrowd brings the creation of the first multilingual automatic speech recognition benchmark in Indonesian and its local languages. Our work is intended to help advance natural language processing research in under-represented languages.

Self-supervised pre-trained transformers have improved the state of the art on a variety of speech tasks. Due to the quadratic time and space complexity of self-attention, they usually operate at the level of relatively short (e.g., utterance) segments. In this paper, we study the use of context, i.e., surrounding segments, during fine-tuning and propose a new approach called context-aware fine-tuning. We attach a context module on top of the last layer of a pre-trained model to encode the whole segment into a context embedding vector which is then used as an additional feature for the final prediction. During the fine-tuning stage, we introduce an auxiliary loss that encourages this context embedding vector to be similar to context vectors of surrounding segments. This allows the model to make predictions without access to these surrounding segments at inference time and requires only a tiny overhead compared to standard fine-tuned models. We evaluate the proposed approach using the SLUE and Librilight benchmarks for several downstream tasks: Automatic speech recognition (ASR), named entity recognition (NER), and sentiment analysis (SA). The results show that context-aware fine-tuning not only outperforms a standard fine-tuning baseline but also rivals a strong context injection baseline that uses neighboring speech segments during inference.

Knowledge-grounded dialogue systems powered by large language models often generate responses that, while fluent, are not attributable to a relevant source of information. Progress towards models that do not exhibit this issue requires evaluation metrics that can quantify its prevalence. To this end, we introduce the Benchmark for Evaluation of Grounded INteraction (BEGIN), comprised of 12k dialogue turns generated by neural dialogue systems trained on three knowledgegrounded dialogue corpora. We collect human annotations assessing the extent to which the models' responses can be attributed to the given background information. We then use BEGIN to analyze eight evaluation metrics. We find that these metrics rely on spurious correlations, do not reliably distinguish attributable abstractive responses from unattributable ones, and perform substantially worse when the knowledge source is longer. Our findings underscore the need for more sophisticated and robust evaluation metrics for knowledge-grounded dialogue. We make BEGIN publicly available at https://github.com/ google/BEGIN-dataset.

这项工作提出了一个新的对话数据集，即cookdial，该数据集促进了对任务知识了解的面向任务的对话系统的研究。该语料库包含260个以人类对任务为导向的对话框，其中代理给出了配方文档，指导用户烹饪菜肴。 Cookdial中的对话框展示了两个独特的功能：（i）对话流与支持文档之间的程序对齐； （ii）复杂的代理决策涉及分割长句子，解释硬说明并在对话框上下文中解决核心。此外，我们在假定的面向任务的对话框系统中确定了三个具有挑战性的（子）任务：（1）用户问题理解，（2）代理操作框架预测和（3）代理响应生成。对于这些任务中的每一个，我们都会开发一个神经基线模型，我们在cookdial数据集上进行了评估。我们公开发布烹饪数据集，包括对话框和食谱文档的丰富注释，以刺激对特定于域的文档接地对话框系统的进一步研究。

We study the capabilities of speech processing systems trained simply to predict large amounts of transcripts of audio on the internet. When scaled to 680,000 hours of multilingual and multitask supervision, the resulting models generalize well to standard benchmarks and are often competitive with prior fully supervised results but in a zero-shot transfer setting without the need for any fine-tuning. When compared to humans, the models approach their accuracy and robustness. We are releasing models and inference code to serve as a foundation for further work on robust speech processing.

Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However, it is also apparent that deep learning based generation is prone to hallucinate unintended text, which degrades the system performance and fails to meet user expectations in many real-world scenarios. To address this issue, many studies have been presented in measuring and mitigating hallucinated texts, but these have never been reviewed in a comprehensive manner before. In this survey, we thus provide a broad overview of the research progress and challenges in the hallucination problem in NLG. The survey is organized into two parts: (1) a general overview of metrics, mitigation methods, and future directions; and (2) an overview of task-specific research progress on hallucinations in the following downstream tasks, namely abstractive summarization, dialogue generation, generative question answering, data-to-text generation, machine translation, and visual-language generation. This survey serves to facilitate collaborative efforts among researchers in tackling the challenge of hallucinated texts in NLG.

Most research on task oriented dialog modeling is based on written text input. However, users interact with practical dialog systems often using speech as input. Typically, systems convert speech into text using an Automatic Speech Recognition (ASR) system, introducing errors. Furthermore, these systems do not address the differences in written and spoken language. The research on this topic is stymied by the lack of a public corpus. Motivated by these considerations, our goal in hosting the speech-aware dialog state tracking challenge was to create a public corpus or task which can be used to investigate the performance gap between the written and spoken forms of input, develop models that could alleviate this gap, and establish whether Text-to-Speech-based (TTS) systems is a reasonable surrogate to the more-labor intensive human data collection. We created three spoken versions of the popular written-domain MultiWoz task -- (a) TTS-Verbatim: written user inputs were converted into speech waveforms using a TTS system, (b) Human-Verbatim: humans spoke the user inputs verbatim, and (c) Human-paraphrased: humans paraphrased the user inputs. Additionally, we provided different forms of ASR output to encourage wider participation from teams that may not have access to state-of-the-art ASR systems. These included ASR transcripts, word time stamps, and latent representations of the audio (audio encoder outputs). In this paper, we describe the corpus, report results from participating teams, provide preliminary analyses of their results, and summarize the current state-of-the-art in this domain.

以查询为中心的摘要（QFS）旨在产生应答感兴趣的特定问题的摘要，从而实现更大的用户控制和个性化。虽然最近发布的数据集如QMSUM或Aquamuse，促进QFS中的研究工作，但该领域缺乏对适用建模方法的广泛空间的全面研究。在本文中，考虑到两种普遍的方法，我们对QFS进行了系统探索，探讨了QFS：两阶段的采掘解决方案和端到端模型。在这些类别中，我们调查现有方法，并呈现了在QMSUM数据集上实现最先进的性能的两个模型扩展，其边缘高达3.38 Rouge-1，3.72 Rouge-2和3.28 Rouge-L。通过定量实验，我们突出了不同模型配置之间的权衡，并探讨了摘要任务之间的转移能力。代码和检查点公开可用：https：//github.com/salesforce/query-focused-sum。

In this work, we introduce IndicXTREME, a benchmark consisting of nine diverse tasks covering 18 languages from the Indic sub-continent belonging to four different families. Across languages and tasks, IndicXTREME contains a total of 103 evaluation sets, of which 51 are new contributions to the literature. To maintain high quality, we only use human annotators to curate or translate\footnote{for IndicXParaphrase, where an automatic translation system is used, a second human verification and correction step is done.} our datasets. To the best of our knowledge, this is the first effort toward creating a standard benchmark for Indic languages that aims to test the zero-shot capabilities of pretrained language models. We also release IndicCorp v2, an updated and much larger version of IndicCorp that contains 20.9 billion tokens in 24 languages. We pretrain IndicBERT v2 on IndicCorp v2 and evaluate it on IndicXTREME to show that it outperforms existing multilingual language models such as XLM-R and MuRIL.

端到端（E2E）模型已成为最新语音识别系统的默认选择。此类型号经过大量标记数据的培训，这些数据通常无法用于低资源语言。诸如自我监督学习和转移学习的诺言之类的技术尚未在培训准确的模型中有效。另一方面，在各种域和扬声器集合上收集标记的数据集非常昂贵。在这项工作中，我们通过公共资料中的印度语言，特别是来自印度广播电台的公共档案馆的印度语言的``采矿''文本和音频对展示了这些方法的廉价和有效替代方案。作为关键组件，我们将Needleman-Wunsch算法调整为与相应的音频片段对齐句子，并给定长音频和其转录本的PDF，同时由于OCR，无关紧要的文本和未转录的语音而对错误进行了强大的态度。因此，我们创建了Shrutilipi，这是一个数据集，其中包含超过6,400个小时的12个印度语言标签的音频，总计为495万个句子。平均而言，Shrutilipi导致2.3倍增加了公开可用的标签数据。我们在12种语言中与21种人类评估者建立了Shrutilipi的质量。我们还根据代表区域，说话者和提到的实体建立了Shrutilipi的多样性。值得注意的是，我们表明，将Shrutilipi添加到WAV2VEC模型的训练集中，导致在Indicsuperb基准上的7种语言中，平均降低了5.8 \％。对于具有最多基准的印地语（7），平均水平从18.8％下降到13.5％。这种改进扩展到有效的模型：对于构象异构体模型（比WAV2VEC小10倍），我们显示出2.3％的下降。最后，我们通过证明对其进行训练的模型对嘈杂的输入更强大，证明了Shrutilipi的多样性。

口语理解（SLU）是大多数人机相互作用系统中的核心任务。随着智能家居，智能手机和智能扬声器的出现，SLU已成为该行业的关键技术。在经典的SLU方法中，自动语音识别（ASR）模块将语音信号转录为文本表示，自然语言理解（NLU）模块从中提取语义信息。最近，基于深神经网络的端到端SLU（E2E SLU）已经获得了动力，因为它受益于ASR和NLU部分的联合优化，因此限制了管道架构的误差效应的级联反应。但是，对于E2E模型用于预测语音输入的概念和意图的实际语言特性知之甚少。在本文中，我们提出了一项研究，以确定E2E模型执行SLU任务的信号特征和其他语言特性。该研究是在必须处理非英语（此处法语）语音命令的智能房屋的应用领域进行的。结果表明，良好的E2E SLU性能并不总是需要完美的ASR功能。此外，结果表明，与管道模型相比，E2E模型在处理背景噪声和句法变化方面具有出色的功能。最后，更细粒度的分析表明，E2E模型使用输入信号的音调信息来识别语音命令概念。本文概述的结果和方法提供了一个跳板，以进一步分析语音处理中的E2E模型。

在本文中，我们提出了一种新颖的架构，用于直接提取语音到语音摘要Essumm，它是一个无监督的模型，而无需依赖中间转录的文本。与以前的文本演示方法不同，我们旨在直接从语音中生成摘要，而无需转录。首先，根据语音信号的声学特征提取一组较小的语音段。对于每个候选语音段，为潜在的语音表示度度量设计了基于距离的汇总置信度评分。具体来说，我们利用现成的自我监督卷积神经网络来提取RAW Audio的深层语音功能。我们的方法会自动预测具有目标摘要长度的关键信息的最佳语音段序列。两个著名的会议数据集（AMI和ICSI语料库）的广泛结果表明，我们基于语音的直接方法通过未转录的数据提高汇总质量的有效性。我们还观察到，我们的无监督语音方法甚至在需要额外的语音识别的情况下以近期基于成绩单的摘要方法进行表现。

我们介绍了用于插槽，意图分类和虚拟助手评估的大规模数据集 - 数字亚马逊SLU资源包（SLURP）。大规模包含1M现实，平行，标记为虚拟助手的话语，涵盖51种语言，18个域，60个意图和55个插槽。通过任务专业翻译人员将仅英文slurp数据集定位为29属的50种类型多样性的语言来创建大规模。我们还介绍了XLM-R和MT5上的建模结果，包括精确的匹配精度，意图分类精度和插槽填充F1分数。我们已经公开发布了数据集，建模代码和模型。