United States

This study explores the factors that predict hesitations and disfluencies in Hindi spontaneous speech. In particular, we investigate the role of lexical, syntactic, prosodic, and phonological factors. Our results provide corroborating evidence that disfluencies arise due to disruptions at various levels of the planning process, such as lexical retrieval and phonological planning (Goldman-Eisler, 1961; Levelt, 1983; Fraundorf and Watson, 2013), or more broadly due to planning delay (Clark and Fox Tree, 2002). Further, disfluencies increase due to processing load (Bortfeld et al., 2001) and due to strain on working memory (Dammalapati et al., 2021). While previous research (e.g., Bell et al., 2009; Dammalapati et al., 2021) highlights the importance of downstream difficulty on disfluencies, our results suggest that previously encountered difficulties can also lead to disfluencies. Together, these findings highlight the need for increased cross-linguistic research to understand the nature of incrementality and monitoring of the production system across languages.

ACL 2026

Determinants of Hesitation and Disfluency in Hindi Spontaneous Speech

hindi spontaneous speech

disfluency

language production

# Welcome everyone to ACL 2022!

The 60th Annual Meeting of the Association for Computational Linguistics is taking place May 22-27, 2022 as a hybrid event, in Dublin and online. We are happy to welcome all of you to this anniversary edition with an almost 50-50 in-person and virtual participation. 
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In series of recent papers, Anttila, Magri, and their collaborators extend the Greenbergian implicational universals from the categorical to the probabilistic setting and use the resulting implicational universals to argue that ME typologies are less restrictive than the NHG typologies corresponding to the same constraint sets. This paper contributes to this research project. We provide a new formal constraint condition on ME universals, focus on a class of implicational universals that hold ''by transitivity'' in categorical OT and HG as well as in probabilistic NHG, and we use our new formal condition to explain why this class of universals instead fails in probabilistic ME.

MaxEnt fails at reasoning by transitivity

Encoder–decoder neural networks with high-dimensional (e.g. $d$=$300$-–$500$) embeddings and hidden layers can be used to model a variety of morphophonological phenomena as sequence-to-sequence mappings, achieving high accuracy across languages and patterns. We show here that these high-capacity models are overparameterized, at least for the task of morphological inflection, and that simpler and smaller networks can perform near ceiling on the task of inflecting Turkish stems. Moreover these reduced-capacity models encode linguistically relevant information even when they are too small to succeed at the inflectional task.

Empirical lower bounds on encoder-decoder capacity for Turkish inflection

We show that semantic proto-role properties can be used as a tool to measure implicit human perceptions of agency and patiency of entities in human-generated text. First, we demonstrate that silver-generated semantic proto-role property labels are strongly correlated with both human judgment and a probabilistic text-based measure of anthropomorphism. Then, we use our measure to quantify linguistic idiosyncrasies across different AI-related Reddit communities. Our measure shows that subreddits dedicated to discussing AI companionship ascribe higher sentience to "bots" and higher agency to "companies" when compared to other subreddits. This phenomenon reveals not only the unique way in which chatbots are anthropomorphized in such subreddits, but also the users' keen awareness of their power imbalance with the companies that created the chatbots.

Measuring Perceptions of Personhood with Semantic Proto-role Properties

Many kinds of logical systems have been employed in constructing formal languages to model phonological phenomena. A common theme among them is that the systems compile into finite automata. Two questions naturally arise. Can a given phenomenon be described with another logical system? And, if so, what is that description?

To the first question, algebraic techniques are well established through deep connections with logic and automata. To the second, the situation is less clear. Translations from automata are established for first-order and monadic second-order logics under precedence, but these may not translate easily to the simpler systems we often use. Translations for simple cases of restricted propositional logic (strictly local or strictly piecewise languages) are established, but insufficient to describe attested phenomena.

The present work establishes a general way to handle many systems in between. Specifically, we show how to translate between certain kinds of algebraic varieties $\mathbf{V}$ (systems defined by universally satisfied identities) and associated logical systems, then use decomposition to handle classes of the form $\mathbf{V}\ast\mathbf{D}$, where the notion of “symbol” is replaced by “$k$-block”. With this, we handle several (unrestricted) propositional logics, facilitating logical description of natural language.

A Family of Effective Methods for Decompiling Canonical Acceptors, Instantiated for Languages of Dot-Depth One and Tier-Based Extensions

Large language models (LLMs) exhibit remarkable few-shot learning capabilities, yet the role of syntactic structure in demonstration examples remains unexplored. Drawing on psycholinguistic research on structural priming, we investigate whether syntactic patterns in few-shot prompts influence LLM outputs and task performance. We conduct systematic experiments across four model families (Llama, Mistral, Qwen, Gemma) using four syntactic constructions (passive voice, cleft sentences, dative alternation, particle placement). Our results reveal robust syntactic priming effects, with priming strength ranging from 1.3× to 6.4× depending on construction type, indicating that models are substantially more likely to produce constructions matching demonstration syntax. Critically, we find that priming strength shows a positive trend with model size (r = 0.85, p = 0.068), with effects intensifying from 7B to 14B parameter models. We demonstrate that priming is construction-specific rather than reflecting general stylistic preferences, and that priming effects persist across multiple intervening sentences. Analysis across three task types (sentence completion, paraphrase generation, story continuation) reveals that syntactic structure in demonstrations influences output style, and that models produce primed constructions even when the task calls for a different syntactic form. These findings have immediate implications for prompt engineering and reveal that LLMs encode syntactic abstractions beyond surface-level pattern matching. We release our benchmark, SyntaxPrime-ICL, containing controlled examples across multiple constructions for evaluating syntactic priming in few-shot contexts.

Syntactic Priming in Few-Shot Learning: How Demonstration Structure Shapes LLM Performance

Nonsensical and anomalous sentences have been instrumental in the development of computational models of semantic interpretation. A core challenge is to distinguish between what is merely anomalous (but can be interpreted given a supporting context) and what is truly nonsensical. However, it is unclear (a) how nonsensical, rather than merely anomalous, existing datasets are; and (b) how well LLMs can make this distinction. In this paper, we answer both questions by collecting sensicality judgments from human raters and LLMs on sentences from five semantically deviant datasets---both context-free and when providing a context. We find that raters consider most sentences at most anomalous, and only a few as properly nonsensical. We also show that LLMs are substantially skilled in generating plausible contexts for anomalous cases.

Finding Sense in Nonsense with Generated Contexts: Perspectives from Humans and Language Models

Lexical Semantic Change Detection (LSCD) is a complex, lemma-level task, which is usually operationalized based on two subsequently applied usage-level tasks: First, Word-in-Context (WiC) labels are derived for pairs of usages. Then, these labels are represented in a graph on which Word Sense Induction (WSI) is applied to derive sense clusters. Finally, LSCD labels are derived by comparing sense clusters over time. This modularity is reflected in most LSCD datasets and models. It also leads to a large heterogeneity in modeling options and task definitions, which is exacerbated by a variety of dataset versions, preprocessing options and evaluation metrics. This heterogeneity makes it difficult to evaluate models under comparable conditions, to choose optimal model combinations or to reproduce results. Hence, we provide a benchmark repository standardizing LSCD evaluation. Through transparent implementation results become easily reproducible and by standardization different components can be freely combined. The repository reflects the task's modularity by allowing model evaluation for WiC, WSI and LSCD. This allows for careful evaluation of increasingly complex model components providing new ways of model optimization. We use the implemented benchmark to conduct a number of experiments with recent models and systematically improve the state-of-the-art.

The LSCD Benchmark: a Testbed for Diachronic Word Meaning Tasks

Making texts clear and comprehensible has become an increasingly important topic in NLP. A possible strategy to enhance text comprehension is to make implicitly conveyed meaning explicit. To explore the role of explicit vs. implied meaning, we study cases of so-called explicitations, i.e. revisions of text in which implicitly conveyed content is made explicit. Using revision histories from wikiHow, we propose a rule-based approach to extract candidate explicitations and curate a human-annotated dataset in which explicitations are distinguished from insertions of new information. Our analyses show that while the extraction method is effective in retrieving relevant cases, distinguishing explicitations from new information is a challenging and often subjective task, reflecting differences in background knowledge and reasoning. Experimentally, we find off-the-shelf LLMs to achieve promising performance, with inconsistent gains from few-shot prompting and fine-tuning. In contrast, fine-tuned NLI models benefit consistently from supervised training and show stronger robustness under distribution shift. In sum, our findings show that the task is challenging, but also indicate that our annotated dataset contains informative signals that models can learn from, paving the way for further research on explicitations.

Can You Be More Explicit? A Task and Dataset on Explicitations of Implicit Meaning

Evaluating the quality of automatically generated text often relies on LLM-as-a-judge (LLM-judge) methods. While effective, these approaches are computationally expensive and require post-processing. To address these limitations, we build upon ParaPLUIE, a perplexity-based LLM-judge metric that estimates confidence over “Yes/No” answers without generating text. We introduce *-PLUIE, task-specific prompting variants of ParaPLUIE and evaluate their alignment with human judgement. Our experiments show that personalised *-PLUIE achieves stronger correlations with human ratings while maintaining low computational cost.

*-PLUIE: Personalisable metric with Llm Used for Improved Evaluation

Stance detection seeks to determine whether a text expresses a position in favor of, against, or neutral toward a target. Despite advances in neural architectures, performance remains inconsistent across datasets. To better understand these disparities, we analyze over 75K samples from four benchmark datasets using six neural models, focusing on stylistic and 
pragmatic language features rather than architectures or external knowledge. We extract 43 features spanning lexical 
richness, syntactic complexity, affective tone, and hedging, and assess their impact through both Logistic Regression and 
SHAP analyses. Our findings reveal distinct stylistic profiles for each stance: favor is best detected when expressed 
concisely with minimal hedging; against when paired with strong negative emotions and greater lexical variety; and none 
when texts are lexically simple and emotionally neutral. Across classes, errors arise from excessive complexity, mixed 
emotional signals, and overuse of hedging. These results advance understanding of what drives success and failure in 
stance detection.

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MaxEnt fails at reasoning by transitivity

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