逆文本归一化（ITN）是自动语音识别（ASR）中必不可少的后处理步骤。它将数字，日期，缩写和其他符号类别从ASR产生的口头形式转换为其书面形式。人们可以将ITN视为机器翻译任务，并使用神经序列到序列模型来解决它。不幸的是，这种神经模型容易产生可能导致不可接受的错误的幻觉。为了减轻此问题，我们提出了一个单个令牌分类器模型，将ITN视为标记任务。该模型将替换片段分配给每个输入令牌，或将其标记为删除或复制而无需更改。我们提出了基于ITN示例的粒状对齐方式的数据集准备方法。提出的模型不太容易出现幻觉错误。该模型在Google文本归一化数据集上进行了培训，并在英语和俄罗斯测试集上实现了最先进的句子精度。标签和输入单词之间的一对一对应关系可改善模型预测的解释性，简化调试并允许后处理更正。该模型比序列到序列模型更简单，并且在生产设置中更易于优化。准备数据集的模型和代码作为NEMO项目的一部分发布。

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。

Pragmatics is an essential part of communication, but it remains unclear what mechanisms underlie human pragmatic communication and whether NLP systems capture pragmatic language understanding. To investigate both these questions, we perform a fine-grained comparison of language models and humans on seven pragmatic phenomena, using zero-shot prompting on an expert-curated set of English materials. We ask whether models (1) select pragmatic interpretations of speaker utterances, (2) make similar error patterns as humans, and (3) use similar linguistic cues as humans to solve the tasks. We find that the largest models achieve high accuracy and match human error patterns: within incorrect responses, models favor the literal interpretation of an utterance over heuristic-based distractors. We also find evidence that models and humans are sensitive to similar linguistic cues. Our results suggest that even paradigmatic pragmatic phenomena may be solved without explicit representations of other agents' mental states, and that artificial models can be used to gain mechanistic insights into human pragmatic processing.

People constantly use language to learn about the world. Computational linguists have capitalized on this fact to build large language models (LLMs) that acquire co-occurrence-based knowledge from language corpora. LLMs achieve impressive performance on many tasks, but the robustness of their world knowledge has been questioned. Here, we ask: do LLMs acquire generalized knowledge about real-world events? Using curated sets of minimal sentence pairs (n=1215), we tested whether LLMs are more likely to generate plausible event descriptions compared to their implausible counterparts. We found that LLMs systematically distinguish possible and impossible events (The teacher bought the laptop vs. The laptop bought the teacher) but fall short of human performance when distinguishing likely and unlikely events (The nanny tutored the boy vs. The boy tutored the nanny). In follow-up analyses, we show that (i) LLM scores are driven by both plausibility and surface-level sentence features, (ii) LLMs generalize well across syntactic sentence variants (active vs passive) but less well across semantic sentence variants (synonymous sentences), (iii) some, but not all LLM deviations from ground-truth labels align with crowdsourced human judgments, and (iv) explicit event plausibility information emerges in middle LLM layers and remains high thereafter. Overall, our analyses reveal a gap in LLMs' event knowledge, highlighting their limitations as generalized knowledge bases. We conclude by speculating that the differential performance on impossible vs. unlikely events is not a temporary setback but an inherent property of LLMs, reflecting a fundamental difference between linguistic knowledge and world knowledge in intelligent systems.

语法提示有时具有自然语言的单词含义。例如，英语单词顺序规则限制了句子的单词顺序，例如“狗咀嚼骨头”，即使可以从世界知识和合理性中推断出“狗”作为代理人和“骨头”的状态。量化这种冗余的发生频率，以及冗余水平如何在类型上多样化的语言中变化，可以阐明语法的功能和演变。为此，我们在英语和俄语中进行了一个行为实验，并进行了跨语言计算分析，以测量从自然主义文本中提取的及物子句中语法线索的冗余性。从自然发生的句子中提取的主题，动词和物体（按随机顺序和形态标记）提出了英语和俄罗斯说话者（n = 484），并被要求确定哪个名词是该动作的推动者。两种语言的准确性都很高（英语约为89％，俄语为87％）。接下来，我们在类似的任务上训练了神经网络机分类器：预测主题对象三合会中的哪个名义是主题。在来自八个语言家庭的30种语言中，性能始终很高：中位准确性为87％，与人类实验中观察到的准确性相当。结论是，语法提示（例如单词顺序）对于仅在10-15％的自然句子中传达了代理和耐心是必要的。然而，他们可以（a）提供重要的冗余来源，（b）对于传达无法从单词中推断出的预期含义至关重要，包括对人类互动的描述，在这些含义中，角色通常是可逆的（例如，雷（Ray）帮助lu/ Lu帮助雷），表达了非典型的含义（例如，“骨头咀嚼狗”。）。