United States

We present ongoing research on automatic post-processing approaches to enhance the readability of noisy speech transcripts in low-resource languages, with a focus on conversational speech in Latvian. We compare transformer-based sequence-labeling models and large language models (LLMs) for the standard punctuation and capitalization restoration task, while also considering automatic correction of mispronounced words and disfluency, and partial inverse text normalization. Our results show that very small LLMs (approx. 2B parameters), fine-tuned on a modest text corpus, can achieve near state-of-the-art performance, rivaling orders of magnitude larger LLMs. Additionally, we demonstrate that a fine-tuned Whisper model, leveraging acoustic cues, outperforms text-only systems on challenging conversational data, even for a low-resource language. Error analysis reveals recurring pitfalls in sentence boundary determination and disfluency handling, emphasizing the importance of consistent annotation and domain adaptation for robust post-processing. Our findings highlight the feasibility of developing efficient post-processing solutions that significantly refine ASR output in low-resource settings, while opening new possibilities for editing and formatting speech transcripts beyond mere restoration of punctuation and capitalization.

NAACL 2025

From Conversational Speech to Readable Text: Post-Processing Noisy Transcripts in a Low-Resource Setting

latvian

low-resource language processing

punctuation restoration

automatic speech recognition

large language models

### Welcome to IJCNLP-AACL 2025! 
 It is a great honor to host this joint conference in Mumbai, India, from December 20 to 24, 2025. The joint conferences of IJCNLP and AACL are organized with alternating leadership in the Asia-Pacific region. The event is run by the Asian Federation of Natural Language Processing (AFNLP) in odd years, and by AACL in even years, while it is organized solely by ACL when the annual ACL meeting is held in the region. This year, the conference is primarily organized by AFNLP. 
*Kentaro Inui
MBZUAI, UAE
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IJCNLP-AACL 2025

The 14th IJCNLP & 4th AACL will be held in Mumbai, India from December 20th to December 24th, 2025.

workshop paper

### Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

Welcome to the 2025 meeting of the Nations of the Americas Chapter of the Association for Computational Linguistics! I am proud to help organize the first NAACL conference to carry the new name of our organization, one that emphasizes inclusion for all of the Americas. I am also pleased to welcome you to Albuquerque, New Mexico, a state whose unique blend of cultural influences will make for an excellent backdrop for NAACL 2025, especially with this year’s special theme on NLP in a Multicultural World. 
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Welcome to 2025 Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics

We manually normalize noisy Japanese expressions on social networking services (SNS) to improve the performance of sentiment polarity classification.Despite advances in pre-trained language models, informal expressions found in social media still plague natural language processing.In this study, we analyzed 6,000 posts from a sentiment analysis corpus for Japanese SNS text, and constructed a text normalization taxonomy consisting of 33 types of editing operations.Text normalization according to our taxonomy significantly improved the performance of BERT-based sentiment analysis in Japanese.Detailed analysis reveals that most types of editing operations each contribute to improve the performance of sentiment analysis.

Text Normalization for Sentiment Analysis in Japanese Social Media

Large language models (LLMs) serve as the foundation of numerous AI applications today. However, despite their remarkable proficiency in generating coherent text, questions linger regarding their ability in performing fine-grained linguistic annotation tasks, such as detecting nouns or verbs, or identifying more complex syntactic structures like clauses or T-units in input texts. These tasks require precise syntactic and semantic understanding of input text, and when LLMs underperform on specific linguistic structures, it raises concerns about their reliability for detailed linguistic analysis and whether their (even correct) outputs truly reflect an understanding of the inputs. In this paper, we empirically study recent LLMs performance across fine-grained linguistic annotation tasks. Through a series of experiments, we find that recent LLMs show limited efficacy in addressing linguistic queries and often struggle with linguistically complex inputs. We show that the most capable LLM (Llama3-70b) makes notable errors in detecting linguistic structures, such as misidentifying embedded clauses, failing to recognize verb phrases, and confusing complex nominals with clauses. Our study provides valuable insights to inform future endeavors in LLM design and development.

Linguistic Blind Spots of Large Language Models

We introduce ParaBLoCC, the Parallel Basic Locative Construction Corpus, the first multilingual compendium of this important grammatico-functional construction, and particularly the first such corpus containing semantically equivalent BLCs in source/target language pairs. The data -- taken from bitext corpora in English paired with twenty-six typologically diverse languages -- are likely to prove useful for studying questions of cognitive underpinnings and cross-linguistic usage patterns of spatial expressions, as well as for improving multilingual spatial relation extraction and related tasks. The data are being made available at https://github.com/pviechnicki/parablocc.

ParaBLoCC: Parallel Basic Locative Constructions Corpus

A parser for Minimalist grammars (Stabler, 2013) has been shown to successfully model sentence processing preferences across an array of languages and phenomena when combined with complexity metrics that relate parsing behavior to memory usage (Gerth, 2015; Graf et al., 2017; De Santo, 2020, a.o.). This model provides a quantifiable theory of the effects of fine-grained grammatical structure on cognitive cost, and can help strengthen the link between generative syntactic theory and sentence processing. However, work on it has focused on offline asymmetries. Here, we extend this approach by showing how memory-based measures of effort that explicitly consider minimalist-like structure-building operations improve our ability to account for word-by-word (online) behavioral data.

Capturing Online SRC/ORC Effort with Memory Measures from a Minimalist Parser

Comedy serves as a profound reflection of the times we live in and is a staple element of human interactions. In light of the widespread adoption of Large Language Models (LLMs), the intersection of humor and AI has become no laughing matter. Advancements in the naturalness of human-computer interaction correlates with improvements in AI systems’ abilities to understand humor. In this study, we assess the ability of models in accurately identifying humorous quotes from a stand-up comedy transcript. Stand-up comedy's unique comedic narratives make it an ideal dataset to improve the overall naturalness of comedic understanding. We propose a novel humor detection metric designed to evaluate LLMs amongst various prompts on their capability to extract humorous punchlines. The metric has a modular structure that offers three different scoring methods - fuzzy string matching, sentence embedding, and subspace similarity - to provide an overarching assessment of a model's performance. The model's results are compared against those of human evaluators on the same task. Our metric reveals that regardless of prompt engineering, leading models, ChatGPT, Claude, and DeepSeek, achieve scores of at most 51% in humor detection. Notably, this performance surpasses that of humans who achieve a score of 41%. The analysis of human evaluators and LLMs reveals variability in agreement, highlighting the subjectivity inherent in humor and the complexities involved in extracting humorous quotes from live performance transcripts.

From Punchlines to Predictions: A Metric to Assess LLM Performance in Identifying Humor in Stand-Up Comedy

We investigate the performance of state-of-the-art (SotA) neural grammar induction (GI) models on a morphemically tokenised English dataset based on the CHILDES treebank (Pearl and Sprouse, 2013). Using implementations from Yang et al. (2021a), we train models and evaluate them with the standard F1 score. We introduce novel evaluation metrics—depth-of-morpheme and sibling-of-morpheme—which measure phenomena around bound morpheme attachment. Our results reveal that models with the highest F1 scores do not necessarily induce linguistically plausible structures for bound morpheme attachment, highlighting a key challenge for cognitively plausible GI.

Profiling neural grammar induction on morphemically tokenised child-directed speech

Reading, while structured, is a non-linear process. Readers may skip some words, linger on others, or revisit earlier text. Emerging work has started exploring the incorporation of reading behaviour through eye-tracking into the training of specific language tasks. In this work, we investigate the broader question of how gaze data can shape word embeddings by using text as read by human participants and predicting gaze measures from them. To that end, we conducted an eye-tracking experiment with 76 participants reading 20 short stories in Spanish and fine-tuned Word2Vec and LSTM models on the collected data. Evaluations with representational similarity analysis and word pair similarities showed a limited, but largely consistent, gain from gaze incorporation, suggesting future work should expand linguistic diversity and use cognitively aligned evaluations to better understand its role in bridging computational and human language representations.

Exploring the Integration of Eye Movement Data on Word Embeddings

There are strong constraints on the structure of a possible lexicon. For example, the negative correlation between word frequency and length known as Zipf's law, and a negative correlation between word length and phonotactic complexity appear to hold across languages. While lexical trade-offs like these have been examined individually, it is unclear how they interact as a system. In this paper, we propose causal discovery as a method for identifying lexical biases and their interactions in a set of variables. We represent the lexicon as a causal model, and apply the Fast Causal Discovery algorithm (Spirtes et al., 1995) to identify both causal relationships between measured variables and the existence of possible unmeasured confounding variables. We apply this method to lexical data including measures of word length, frequency, phonotactic complexity, and morphological irregularity for 25 languages and find evidence of universal associations involving word length with a high likelihood of involving an unmeasured confounder, suggesting that additional variables need to be measured to determine how they are related. We also find evidence of variation across languages in relationships between the remaining variables, and suggest that given a larger dataset, causal discovery algorithms can be a useful tool in assessing the universality of lexical biases.

Unzipping the Causality of Zipf’s Law and Other Lexical Trade-offs

Although functional specialization in the brain - a phenomenon where different regions process different types of information - is well documented, we still lack precise mathematical methods with which to measure it. This work proposes a technique to quantify how brain regions respond to distinct categories of information. Using a topic encoding model, we identify brain regions that respond strongly to specific semantic categories while responding minimally to all others. We then use a language model to characterize the common themes across each region's preferred categories. Our technique successfully identifies previously known functionally selective regions and reveals consistent patterns across subjects while also highlighting new areas of high specialization worthy of further study.

Quantifying Semantic Functional Specialization in the Brain Using Encoding Models of Natural Language

Alzheimer's Disease (AD) dementia is a progressive neurodegenerative disease that negatively impacts patients' cognitive ability. Previous studies have demonstrated that changes in naturalistic language samples can be useful for early screening of AD dementia. However, the nature of language deficits often requires test administrators to use various speech elicitation techniques during spontaneous language assessments to obtain enough propositional utterances from dementia patients. This could lead to the ``observer's effect'' on the downstream analysis that has not been fully investigated. Our study seeks to quantify the influence of test administrators on linguistic features in dementia assessment with two English corpora the ``Cookie Theft'' picture description datasets collected at different locations and test administrators show different levels of administrator involvement. Our results show that the level of test administrator involvement significantly impacts observed linguistic features in patient speech. These results suggest that many of significant linguistic features in the downstream classification task may be partially attributable to differences in the test administration practices rather than solely to participants' cognitive status. The variations in test administrator behavior can lead to systematic biases in linguistic data, potentially confounding research outcomes and clinical assessments. Our study suggests that there is a need for a more standardized test administration protocol in the development of responsible clinical speech analytics frameworks.

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Text Normalization for Sentiment Analysis in Japanese Social Media

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