Malta

Text data augmentation is a complex problem due to the discrete nature of sentences. Although rule-based augmentation methods are widely adopted in real-world applications because of their simplicity, they suffer from potential semantic damage. Previous researchers have suggested easy data augmentation with soft labels (softEDA), employing label smoothing to mitigate this problem. However, finding the best factor for each model and dataset is challenging; therefore, using softEDA in real-world applications is still difficult. In this paper, we propose adapting AutoAugment to solve this problem. The experimental results suggest that the proposed method can boost existing augmentation methods and that rule-based methods can enhance cutting-edge pretrained language models. We offer the source code.

EACL 2024

AutoAugment Is What You Need: Enhancing Rule-based Augmentation Methods in Low-resource Regimes

text classification

data augmentation

low-resource

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ACL 2023

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

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Welcome to the 18th Conference of the European Chapter of the Association for Computational Linguistics (EACL). Continuing its mission of expanding and involving the science community of all European countries, EACL has selected the Malta community for the 18th EACL. Considering the importance of physical interaction among researchers, the conference will be held at the Hotel Radisson Blu, St. Julians, in Malta, from 17 to 22 of March, 2024. The conference will also feature the possibility to participate virtually. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language. 
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As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

The goal of this paper is to evaluate the performance of Stanza, a part-of-speech (POS) tagger developed for modern Polish, on historical text to assess its possible use for automating the annotation of other historical texts. While the issue of the reliability of utilizing POS taggers on historical data has been previously discussed, most of the research focuses on languages whose grammar differs from Polish, meaning that their results need not be fully applicable in this case. The evaluation of Stanza is conducted on two sets of 10286 and 3270 manually annotated tokens from a piece of historical Polish writing (1899), and the errors are analyzed qualitatively and quantitatively. The results show a good performance of the tagger, especially when it comes to Universal Part-of-Speech (UPOS) tags, which is promising for utilizing the tagger for automatic annotation in larger projects, and pinpoint some common features of misclassified tokens.

Can Stanza be Used for Part-of-Speech Tagging Historical Polish?

Hallucinations pose a significant challenge to the reliability and alignment of Large Language Models (LLMs), limiting their widespread acceptance beyond chatbot applications. Despite ongoing efforts, hallucinations remain a prevalent challenge in LLMs. The detection of hallucinations itself is also a formidable task, frequently requiring manual labeling or constrained evaluations. This paper introduces an automated scalable framework that combines benchmarking LLMs' hallucination tendencies with efficient hallucination detection. We leverage LLMs to generate challenging tasks related to hypothetical phenomena, subsequently employing them as agents for efficient hallucination detection. The framework is domain-agnostic, allowing the use of any language model for benchmark creation or evaluation in any domain. We introduce the publicly available HypoTermQA Benchmarking Dataset, on which state-of-the-art models' performance ranged between 3% and 11%, and evaluator agents demonstrated a 6% error rate in hallucination prediction. The proposed framework provides opportunities to test and improve LLMs. Additionally, it has the potential to generate benchmarking datasets tailored to specific domains, such as law, health, and finance.

HypoTermQA: Hypothetical Terms Dataset for Benchmarking Hallucination Tendency of LLMs

When humans perceive objects, they express their material and sensory impressions using language. This paper focuses on the Japanese concept of shitsukan, which encompasses the entire sensory experience when perceiving objects, and extends it to natural language processing (NLP). In this study, we collected shitsukan terms recalled by individuals in response to object images and object names through crowdsourcing, creating a relatively large dataset comprising approximately 
data points. In addition, we designed benchmark tasks using the collected data and discussed insights into the current shitsukan recognition capabilities of Large Language Models (LLMs) and Large Vision-Language Models (LVLMs). Some LLMs showed a degree of accuracy in understanding shitsukan terms. Furthermore, while there is a low similarity between shitsukan terms generated by the model and those generated by humans, manual evaluations confirmed that the model is capable of generating shitsukan terms that appear natural to human observers to some extent.

Exploring Large Language Models' Understanding of Shitsukan

The linguistic validity of Combinatory Categorial Grammar (CCG) parsing results relies heavily on treebanks for training and evaluation, so the treebank construction is crucial. Yet the current Japanese CCG treebank is known to have inaccuracies in its analyses of Japanese syntactic structures, including passive and causative constructions. While ABCTreebank, a treebank for ABC grammar, has been made to improve the analysis, particularly of argument structures, it lacks the detailed syntactic features required for Japanese CCG. In contrast, the Japanese CCG parser, lightblue, efficiently provides detailed syntactic features, but it does not accurately capture argument structures. We propose a method to generate a linguistically valid Japanese CCG treebank with detailed information by combining the strengths of ABCTreebank and lightblue. We develop an algorithm that filters lightblue’s lexical items using ABCTreebank, effectively converting lightblue output into a linguistically valid CCG treebank. To evaluate our treebank, we manually evaluate CCG syntactic structures and semantic representations and analyze conversion rates.

Reforging : A Method for Constructing a Linguistically Valid Japanese CCG Treebank

In this work, we focus on the use of sentence translation exercise in educational settings and attempted to automatic scoring.
While Sentence Translation Exercises (STE) are effective in L2 learning, the frequency of exercises is often limited due to the considerable burden of grading.
Therefore, automating the correction and feedback for translation exercises has the potential to significantly transform the educational environment in language learning．
The contributions of this study are the establishment of Sentence Translation Exercises (STE) as a new task in a format ready for practical use, the construction of a dataset according to task design, and the creation of a baseline that shows potential for advancement.

Japanese-English Sentence Translation Exercises Dataset for Automatic Grading

Vision-and-Language Navigation (VLN) task involves navigating mobility using linguistic commands and has application in developing interfaces for autonomous mobility. In reality, natural human communication also encompasses non-verbal cues like hand gestures and gaze. These gesture-guided instructions have been explored in Human-Robot Interaction systems for effective interaction, particularly in object referring expressions. However, a notable gap exists in tackling gesture-based demonstrative expressions in outdoor VLN task. To address this, we introduce a novel dataset for gesture-guided outdoor VLN instructions with demonstrative expressions, designed with a focus on complex instructions requiring multi-hop reasoning between the multiple input modalities. In addition, our work also includes a comprehensive analysis of the collected data and a comparative evaluation against the existing datasets.

GesNavi: Gesture-guided Outdoor Vision-and-Language Navigation

Generating code is an important application of Large Language Models (LLMs) and the task of function completion is one of the core open challenges in this context. Existing approaches focus on either training, fine-tuning or prompting LLMs to generate better outputs given the same input. We propose a novel and complementary approach: to optimize part of the input, the docstring (summary of a function's purpose and usage), via reformulation with an LLM, in order to improve code generation. We develop two baseline methods for optimizing code generation via docstring reformulation and test them on the original HumanEval benchmark and multiple curated variants which are made more challenging by realistically worsening the docstrings. Our results show that, when operating on docstrings reformulated by an LLM instead of the original (or worsened) inputs, the performance of a number of open-source LLMs does not change significantlyThis finding demonstrates an unexpected robustness of current open-source LLMs to the details of the docstrings. We conclude by examining a series of questions, accompanied by in-depth analyses, pertaining to the sensitivity of current open-source LLMs to the details in the docstrings, the potential for improvement via docstring reformulation and the limitations of the methods employed in this work.

Can docstring reformulation with an LLM improve code generation?

Diffusion models have recently shown great potential on many generative tasks. In this work, we explore diffusion models for machine translation (MT). We adapt two prominent diffusion-based text generation models, Diffusion-LM and DiffuSeq, to perform machine translation. As the diffusion models generate non-autoregressively (NAR), we draw parallels to NAR machine translation models. With a comparison to conventional Transformer-based translation models, as well as to the Levenshtein Transformer, an established NAR MT model, we show that the multimodality problem that limits NAR machine translation performance is also a challenge to diffusion models. We demonstrate that knowledge distillation from an autoregressive model improves the performance of diffusion-based MT. A thorough analysis on the translation quality of inputs of different lengths shows that the diffusion models struggle more on long-range dependencies than other models.

Benchmarking Diffusion Models for Machine Translation

The advancement of generative Large Language Models (LLMs), capable of producing human-like texts, introduces challenges related to the authenticity of the text documents. This requires exploring potential forgery scenarios within the context of authorship attribution, especially in the literary domain. Particularly, two aspects of doubted authorship may arise in novels, as a novel may be imposed by a renowned author or include a copied writing style of a well-known novel. To address these concerns, we introduce Forged-GAN-BERT, a modified GAN-BERT-based model to improve the classification of forged novels in two data-augmentation aspects: via the Forged Novels Generator (i.e., ChatGPT) and the generator in GAN. Compared to other transformer-based models, the proposed Forged-GAN-BERT model demonstrates an improved performance with F1 scores of 0.97 and 0.71 for identifying forged novels in single-author and multi-author classification settings. Additionally, we explore different prompt categories for generating the forged novels to analyse the quality of the generated texts using different similarity distance measures, including ROUGE-1, Jaccard Similarity, Overlap Confident, and Cosine Similarity.

Forged-GAN-BERT: Authorship Attribution for LLM-Generated Forged Novels

Recent advancements in instruction-based language models have demonstrated exceptional performance across various natural language processing tasks.
We present a comprehensive analysis of the performance of two open-source language models, BERT and Llama-2, in the context of dynamic task-oriented dialogues. Focusing on the Restaurant domain and utilizing the MultiWOZ 2.4 dataset, our investigation centers on the models' ability to generate predictions for masked slot values within text. The dynamic aspect is introduced through simulated domain changes, mirroring real-world scenarios where new slot values are incrementally added to a domain over time.
This study contributes to the understanding of instruction-based models' effectiveness in dynamic natural language understanding tasks when compared to traditional language models and emphasizes the significance of open-source, reproducible models in advancing research within the academic community.

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Can Stanza be Used for Part-of-Speech Tagging Historical Polish?

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Text Augmentation Using Dataset Reconstruction for Low-Resource Classification