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Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can generate quantitative priming predictions from contemporary theories in syntax which assume a lexicalized grammar. By generating and testing priming predictions from competing theoretical accounts, we can infer which assumptions from syntactic theory are useful for characterizing the representations humans build when processing sentences. As a case study, we use SPAWN to generate priming predictions from two theories (Whiz-Deletion and Participial-Phase) which make different assumptions about the structure of English relative clauses. By modulating the reanalysis mechanism that the parser uses and strength of the parser&#39;s prior knowledge, we generated nine sets of predictions from each of the two theories. Then, we tested these predictions using a novel web-based comprehension-to-production priming paradigm. We found that while the some of the predictions from the Participial-Phase theory aligned with human behavior, none of the predictions from the the Whiz-Deletion theory did, thus suggesting that the Participial-Phase theory might better characterize human relative clause representations.

EMNLP 2024

SPAWNing Structural Priming Predictions from a Cognitively Motivated Parser

structural priming

cognitive modeling

parsing

Structural priming is a widely used psycholinguistic paradigm to study human sentence representations. In this work we introduce SPAWN, a cognitively motivated parser that can generate quantitative priming predictions from contemporary theories in syntax which assume a lexicalized grammar. By generating and testing priming predictions from competing theoretical accounts, we can infer which assumptions from syntactic theory are useful for characterizing the representations humans build when processing sentences. As a case study, we use SPAWN to generate priming predictions from two theories (Whiz-Deletion and Participial-Phase) which make different assumptions about the structure of English relative clauses. By modulating the reanalysis mechanism that the parser uses and strength of the parser's prior knowledge, we generated nine sets of predictions from each of the two theories. Then, we tested these predictions using a novel web-based comprehension-to-production priming paradigm. We found that while the some of the predictions from the Participial-Phase theory aligned with human behavior, none of the predictions from the the Whiz-Deletion theory did, thus suggesting that the Participial-Phase theory might better characterize human relative clause representations.

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This paper evaluates whether large language models (LLMs) exhibit cognitive fan effects, similar to those discovered by Anderson in humans, after being pre-trained on human textual data. We conduct two sets of in-context recall experiments designed to elicit fan effects. Consistent with human results, we find that LLM recall uncertainty, measured via token probability, is influenced by the fan effect. Our results show that removing uncertainty disrupts the observed effect. The experiments suggest the fan effect is consistent whether the fan value is induced in-context or in the pre-training data. Finally, these findings provide in-silico evidence that fan effects and typicality are expressions of the same phenomena.

Large Language Model Recall Uncertainty is Modulated by the Fan Effect

State of the art models in automatic speech recognition have shown remarkable im- provements due to modern self-supervised (SSL) transformer-based architectures such as wav2vec 2.0 (Baevski et al., 2020). However, how these models encode phonetic informa- tion is still not well understood. We explore whether SSL speech models display a linguis- tic property that characterizes human speech perception: language specificity. We show that while wav2vec 2.0 displays an overall lan- guage specificity effect when tested on Hindi vs. English, it does not resemble human speech perception when tested on finer-grained differ- ences in Hindi speech contrasts.

Self-supervised speech representations display some human-like cross-linguistic perceptual abilities

Grapheme-to-Phoneme (G2P) correspondences form foundational frameworks of tasks such as text-to-speech (TTS) synthesis or automatic speech recognition. The G2P process involves taking words in their written form and generating their pronunciation. In this paper, we critique the status quo definition of a grapheme, currently a forced alignment process relating a single character to either a phoneme or a blank unit, that underlies the majority of modern approaches. We develop a linguisticallymotivated redefinition from simple concepts such as vowel and consonant count and word length and offer a proof-of-concept implementation based on a multi-binary neural classification task. Our model achieves competitive results with a 31.86% Word Error Rate on a standard benchmark, while generating linguistically meaningful grapheme segmentations.

One-Vs-Rest Neural Network English Grapheme Segmentation: A Linguistic Perspective

Counterspeech presents a viable alternative to banning or suspending users for hate speech while upholding freedom of expression. How- ever, writing effective counterspeech is chal- lenging for moderators/users. Hence, develop- ing suggestion tools for writing counterspeech is the need of the hour. One critical challenge in developing such a tool is the lack of qual- ity and diversity of the responses in the ex- isting datasets. Hence, we introduce a new dataset - CROWDCOUNTER containing 3,425 hate speech-counterspeech pairs spanning six different counterspeech types (empathy, humor, questioning, warning, shaming, contradiction), which is the first of its kind. The design of our annotation platform itself encourages an- notators to write type-specific, non-redundant and high-quality counterspeech. We evaluate two frameworks for generating counterspeech responses - vanilla and type-controlled prompts - across four large language models. In terms of metrics, we evaluate the responses using rel- evance, diversity and quality. We observe that Flan-T5 is the best model in the vanilla frame- work across different models. Type-specific prompts enhance the relevance of the responses, although they might reduce the language qual- ity. DialoGPT proves to be the best at following the instructions and generating the type-specific counterspeech accurately.

CrowdCounter: A benchmark type-specific multi-target counterspeech dataset

LLMs are increasingly being deployed for multilingual applications and have demonstrated impressive translation capabilities between several low and high-resource languages. An aspect of translation that often gets overlooked is that of cultural adaptation, or modifying source culture references to suit the target culture. While specialized translation models still outperform LLMs on the machine translation task when viewed from the lens of correctness, they are not sensitive to cultural differences often requiring manual correction. LLMs on the other hand have a rich reservoir of cultural knowledge embedded within its parameters that can be potentially exploited for such applications. In this paper, we define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs for cultural adaptation and analyze their cross-cultural knowledge while connecting related concepts across different cultures. We also analyze possible issues with automatic adaptation. We hope that this task will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.

Translating Across Cultures: LLMs for Intralingual Cultural Adaptation

Although pre-trained language models (PLMs) are effective for natural language understanding (NLU) tasks, they demand a huge computational resource, thus preventing us from deploying them on edge devices. Researchers have therefore applied compression techniques for neural networks, such as pruning, quantization, and knowledge distillation, to the PLMs. Although these generic techniques can reduce the number of internal parameters of hidden layers in the PLMs, the embedding layers tied to the tokenizer are hard to compress, occupying a non-negligible portion of the compressed model. In this study, aiming to further compress PLMs reduced by the generic techniques, we exploit frequency-aware sparse coding to compress the embedding layers of the PLMs fine-tuned to downstream tasks. To minimize the impact of the compression on the accuracy, we retain the embeddings of common tokens as they are and use them to reconstruct embeddings of rare tokens by locally linear mapping. Experimental results on the GLUE and JGLUE benchmarks for language understanding in English and Japanese confirmed that our method can further compress the fine-tuned DistilBERT models models while maintaining accuracy.

Further Compressing Distilled Language Models via Frequency-aware Partial Sparse Coding of Embeddings

This study introduces Simple Reasoning with Code (SiRC), a novel instruction fine-tuning method for solving mathematical reasoning problems, particularly effective for Vietnamese, which is considered a low-resource language. Specifically, solving mathematical problems requires strategic and logical reasoning, which remains challenging in this research area. This paper presents a simple yet effective instruction fine-tuning method for mathematical reasoning. Unlike previous approaches, our proposed method effectively combines chain-of-thought reasoning with code transfer methods without requiring a sophisticated inference procedure. Furthermore, we focus on exploiting small open-source large language models (LLMs) for the Vietnamese language. In this regard, we first introduce a trainable Vietnamese math reasoning dataset, which is named ViMath-InstructCode. The proposed dataset is then used for fine-tuning open-source LLMs (e.g., less than 10 billion parameters). Experiments conducted on our custom ViMath-Ben

A Novel Instruction Tuning Method for Vietnamese Mathematical Reasoning using Trainable Open-Source Large Language Models

Large language models (LLMs) have achieved significant success in complex tasks across various domains, but they come with high computational costs and inference latency issues. Pruning, as an effective method, can significantly reduce inference costs. However, current pruning algorithms for encoder-based language models often focus on locally optimal solutions, neglecting a comprehensive exploration of the global solution space. This oversight can lead to instability in the solution process, thereby affecting the overall performance of the model. To address these challenges, we propose a structured pruning algorithm named G-Pruner (Global Pruner), comprising two integral components: PPOM (Proximal Policy Optimization Mask) and CG²MT (Conjugate Gradient Squared Mask Tuning), utilizing a global optimization strategy. This strategy not only eliminates the need for retraining but also ensures the algorithm's stability and adaptability to environmental changes, effectively addressing the issue of focusing solely

Global-Pruner: A Stable and Efficient Pruner for Retraining-Free Pruning of Encoder-Based Language Models

This paper presents the results of the patent translation shared task at the 11th Workshop on Asian Translation and 9th Conference on Machine Translation. Two teams participated in this task, and their submitted translation results for one or more of the six language directions were automatically and manually evaluated. The evaluation results demonstrate the strong performance of large language modelbased systems from both participants.

Results of the WAT/WMT 2024 Shared Task on Patent Translation

We present the joint CUNI and LMU submission to the MRL~2024 Shared Task on Multi-lingual Multi-task Information Retrieval. The shared task objective was to explore how we can deploy modern methods in NLP in multi-lingual low-resource settings, tested on two sub-tasks: Named-entity recognition and question answering. Our solutions to the subtasks are based on data acquisition and model adaptation. We compare the performance of our submitted systems with the translate-test approach which proved to be the most useful in the previous edition of the shared task. Our results show that using more data as well as fine-tuning recent multilingual pre-trained models leads to considerable improvements over the translate-test baseline. Our code is available at https://github.com/ufal/mrl2024-multilingual-ir-shared-task.

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