ymoslem/acl-abstracts huggingface dataset:
TP-Link: Fine-grained Pre-Training for Text-to-SQL Parsing with Linking Information (2024)

In this paper, we introduce an innovative pre-training framework TP-Link, which aims to improve context-dependent Text-to-SQL Parsing by leveraging Linking information. This enhancement is achieved through better representation of both natural language utterances and the database schema, ultimately facilitating more effective text-to-SQL conversations. We present two novel pre-training objectives: (i) utterance linking prediction (ULP) task that models intricate syntactic relationships among natural language utterances in context-dependent text-to-SQL scenarios, and (ii) schema linking prediction (SLP) task that focuses on capturing fine-grained schema linking relationships between the utterances and the database schema. Extensive experiments demonstrate that our proposed TP-Link achieves state-of-the-art performance on two leading downstream benchmarks (i.e., SParC and CoSQL).
Training BERT Models to Carry over a Coding System Developed on One Corpus to Another (2024)

This paper describes how we train BERT models to carry over a coding system developed on the paragraphs of a Hungarian literary journal to another. The aim of the coding system is to track trends in the perception of literary translation around the political transformation in 1989 in Hungary. To evaluate not only task performance but also the consistence of the annotation, moreover, to get better predictions from an ensemble, we use 10-fold crossvalidation. Extensive hyperparameter tuning is used to obtain the best possible results and fair comparisons. To handle label imbalance, we use loss functions and metrics robust to it. Evaluation of the effect of domain shift is carried out by sampling a test set from the target domain. We establish the sample size by estimating the bootstrapped confidence interval via simulations. This way, we show that our models can carry over one annotation system to the target domain. Comparisons are drawn to provide insights such as learning multilabel correlations and confidence penalty improve resistance to domain shift, and domain adaptation on OCR-ed text on another domain improves performance almost to the same extent as that on the corpus under study. See our code at https://codeberg.org/zsamboki/bert-annotator-ensemble
TransCoder: Towards Unified Transferable Code Representation Learning Inspired by Human Skills (2024)

Code pre-trained models (CodePTMs) have recently demonstrated a solid capacity to process various code intelligence tasks, e.g., code clone detection, code translation, and code summarization. The current mainstream method that deploys these models to downstream tasks is to fine-tune them on individual tasks, which is generally costly and needs sufficient data for large models. To tackle the issue, in this paper, we present TransCoder, a unified Transferable fine-tuning strategy for Code representation learning. Inspired by human inherent skills of knowledge generalization, TransCoder drives the model to learn better code-related knowledge like human programmers. Specifically, we employ a tunable prefix encoder to first capture cross-task and cross-language transferable knowledge, subsequently applying the acquired knowledge for optimized downstream adaptation. Besides, our approach confers benefits for tasks with minor training sample sizes and languages with smaller corpora, underscoring versatility and efficacy. Extensive experiments conducted on representative datasets clearly demonstrate that our method can lead to superior performance on various code-related tasks and encourage mutual reinforcement, especially in low-resource scenarios. Our codes are available at https://github.com/QiushiSun/TransCoder.
TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotation (2024)

This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at \url{https://github.com/dellixx/TransERR}.
Transfer Fine-tuning for Quality Estimation of Text Simplification (2024)

To efficiently train quality estimation of text simplification on a small-scale labeled corpus, we train sentence difficulty estimation prior to fine-tuning the pre-trained language models. Our proposed method improves the quality estimation of text simplification in the framework of transfer fine-tuning, in which pre-trained language models can improve the performance of the target task by additional training on the relevant task prior to fine-tuning. Since the labeled corpus for quality estimation of text simplification is small (600 sentence pairs), an efficient training method is desired. Therefore, we propose a training method for pseudo quality estimation that does not require labels for quality estimation. As a relevant task for quality estimation of text simplification, we train the estimation of sentence difficulty. This is a binary classification task that identifies which sentence is simpler using an existing parallel corpus for text simplification. Experimental results on quality estimation of English text simplification showed that not only the quality estimation performance on simplicity that was trained, but also the quality estimation performance on fluency and meaning preservation could be improved in some cases.
Transferring BERT Capabilities from High-Resource to Low-Resource Languages Using Vocabulary Matching (2024)

Pre-trained language models have revolutionized the natural language understanding landscape, most notably BERT (Bidirectional Encoder Representations from Transformers). However, a significant challenge remains for low-resource languages, where limited data hinders the effective training of such models. This work presents a novel approach to bridge this gap by transferring BERT capabilities from high-resource to low-resource languages using vocabulary matching. We conduct experiments on the Silesian and Kashubian languages and demonstrate the effectiveness of our approach to improve the performance of BERT models even when the target language has minimal training data. Our results highlight the potential of the proposed technique to effectively train BERT models for low-resource languages, thus democratizing access to advanced language understanding models.
Transformer-based Joint Modelling for Automatic Essay Scoring and Off-Topic Detection (2024)

Automated Essay Scoring (AES) systems are widely popular in the market as they constitute a cost-effective and time-effective option for grading systems. Nevertheless, many studies have demonstrated that the AES system fails to assign lower grades to irrelevant responses. Thus, detecting the off-topic response in automated essay scoring is crucial in practical tasks where candidates write unrelated text responses to the given task in the question. In this paper, we are proposing an unsupervised technique that jointly scores essays and detects off-topic essays. The proposed Automated Open Essay Scoring (AOES) model uses a novel topic regularization module (TRM), which can be attached on top of a transformer model, and is trained using a proposed hybrid loss function. After training, the AOES model is further used to calculate the Mahalanobis distance score for off-topic essay detection. Our proposed method outperforms the baseline we created and earlier conventional methods on two essay-scoring datasets in off-topic detection as well as on-topic scoring. Experimental evaluation results on different adversarial strategies also show how the suggested method is robust for detecting possible human-level perturbations.
Transformer-based Swedish Semantic Role Labeling through Transfer Learning (2024)

Semantic Role Labeling (SRL) is a task in natural language understanding where the goal is to extract semantic roles for a given sentence. English SRL has achieved state-of-the-art performance using Transformer techniques and supervised learning. However, this technique is not a viable choice for smaller languages like Swedish due to the limited amount of training data. In this paper, we present the first effort in building a Transformer-based SRL system for Swedish by exploring multilingual and cross-lingual transfer learning methods and leveraging the Swedish FrameNet resource. We demonstrate that multilingual transfer learning outperforms two different cross-lingual transfer models. We also found some differences between frames in FrameNet that can either hinder or enhance the model`s performance. The resulting end-to-end model is freely available and will be made accessible through Spr{\r{a}}kbanken Text`s research infrastructure.
Transformers for Bridging Persian Dialects: Transliteration Model for Tajiki and Iranian Scripts (2024)

In this study, we address the linguistic challenges posed by Tajiki Persian, a distinct variant of the Persian language that utilizes the Cyrillic script due to historical {\textquotedblleft}Russification{\textquotedblright}. This distinguishes it from other Persian dialects that adopt the Arabic script. Despite its profound linguistic and cultural significance, Tajiki Persian remains a low-resource language with scant digitized datasets for computational applications. To address this deficiency, we created a parallel corpus using Shahnameh, a seminal Persian epic poem. Employing optical character recognition, we extracted Tajiki Persian verses from primary sources and applied a heuristic method to align them with their Iranian Persian counterparts. We then trained and assessed transliteration models using two prominent sequence-to-sequence architectures: GRU with attention and transformer. Our results underscore the enhanced performance of our models, particularly in contrast to pre-trained large multilingual models like GPT-3.5, emphasizing the value of dedicated datasets in advancing computational approaches for underrepresented languages. With the publication of this work, we are disseminating, for the first time, a vast collection of Persian poetry spanning 1000 years, transcribed in Tajiki scripts for the benefit of the Tajiki-speaking communities. The dataset, along with the model`s code and checkpoints, is accessible at https://github.com/language-ml/Tajiki-Shahname, marking a significant contribution to computational linguistic resources for Tajiki Persian.
Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment (2024)

Training large language models (LLMs) with open-domain instruction data has yielded remarkable success in aligning to end tasks and human preferences. Extensive research has highlighted the importance of the quality and diversity of instruction data. However, the impact of data complexity, as a crucial metric, remains relatively unexplored from three aspects: (1)where the sustainability of performance improvements with increasing complexity is uncertain; (2)whether the improvement brought by complexity merely comes from introducing more training tokens; and (3)where the potential benefits of incorporating instructions from easy to difficult are not yet fully understood. In this paper, we propose Tree-Instruct to systematically enhance the instruction complexity in a controllable manner. By adding a specified number of nodes to instructions' semantic trees, this approach not only yields new instruction data from the modified tree but also allows us to control the difficulty level of modified instructions. Our preliminary experiments reveal the following insights: (1)Increasing complexity consistently leads to sustained performance improvements of LLMs. (2)Under the same token budget, a few complex instructions outperform diverse yet simple instructions. (3)Curriculum instruction tuning might not yield the anticipated results; focusing on increasing complexity appears to be the key.
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024)

KEPLMs are pre-trained models that utilize external knowledge to enhance language understanding. Previous language models facilitated knowledge acquisition by incorporating knowledge-related pre-training tasks learned from relation triples in knowledge graphs. However, these models do not prioritize learning embeddings for entity-related tokens. Updating all parameters in KEPLM is computationally demanding. This paper introduces TRELM, a Robust and Efficient Pre-training framework for Knowledge-Enhanced Language Models. We observe that text corpora contain entities that follow a long-tail distribution, where some are suboptimally optimized and hinder the pre-training process. To tackle this, we employ a robust approach to inject knowledge triples and employ a knowledge-augmented memory bank to capture valuable information. Moreover, updating a small subset of neurons in the feed-forward networks (FFNs) that store factual knowledge is both sufficient and efficient. Specifically, we utilize dynamic knowledge routing to identify knowledge paths in FFNs and selectively update parameters during pre-training. Experimental results show that TRELM achieves at least a 50{\%} reduction in pre-training time and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks.
Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks (2024)

Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating their prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies. Limited studies have been conducted to formalize and analyze these attacks and their mitigations. We bridge this gap by proposing a formalism and a taxonomy of known (and possible) jailbreaks. We survey existing jailbreak methods and their effectiveness on open-source and commercial LLMs (such as GPT-based models, OPT, BLOOM, and FLAN-T5-XXL). We further discuss the challenges of jailbreak detection in terms of their effectiveness against known attacks. For further analysis, we release a dataset of model outputs across 3700 jailbreak prompts over 4 tasks.
Triple-R: Automatic Reasoning for Fact Verification Using Language Models (2024)

The rise of online social media platforms has made them a popular source of news. However, they are also prone to misinformation and fake news. To combat this, fact-checking is essential to verify the accuracy of claims made on these platforms. However, the existing methods in this field often lack the use of external sources and human-understandable explanations for system decisions. In this paper, we introduce a framework called Triple-R (Retriever, Ranker, Reasoner) that addresses these challenges. The framework uses the Web as an external knowledge source to retrieve relevant evidence for claims and includes a method to generate reasons based on the retrieved evidence for datasets lacking explanations. We then use this modified dataset to fine-tune a causal language model that generates natural language explanations and labels for pairs of retrieved evidence and claims. Our approach aims to improve the transparency and interpretability of fact-checking systems by providing understandable explanations for decision-making processes. We evaluated our method on a popular dataset and demonstrated its performance through an ablation study. The modified dataset is available on the Canadian Institute for Cybersecurity datasets webpage at https://www.unb.ca/cic/datasets/index.html.
Triples-to-isiXhosa (T2X): Addressing the Challenges of Low-Resource Agglutinative Data-to-Text Generation (2024)

Most data-to-text datasets are for English, so the difficulties of modelling data-to-text for low-resource languages are largely unexplored. In this paper we tackle data-to-text for isiXhosa, which is low-resource and agglutinative. We introduce Triples-to-isiXhosa (T2X), a new dataset based on a subset of WebNLG, which presents a new linguistic context that shifts modelling demands to subword-driven techniques. We also develop an evaluation framework for T2X that measures how accurately generated text describes the data. This enables future users of T2X to go beyond surface-level metrics in evaluation. On the modelling side we explore two classes of methods - dedicated data-to-text models trained from scratch and pretrained language models (PLMs). We propose a new dedicated architecture aimed at agglutinative data-to-text, the Subword Segmental Pointer Generator (SSPG). It jointly learns to segment words and copy entities, and outperforms existing dedicated models for 2 agglutinative languages (isiXhosa and Finnish). We investigate pretrained solutions for T2X, which reveals that standard PLMs come up short. Fine-tuning machine translation models emerges as the best method overall. These findings underscore the distinct challenge presented by T2X: neither well-established data-to-text architectures nor customary pretrained methodologies prove optimal. We conclude with a qualitative analysis of generation errors and an ablation study.
Trustworthiness and Self-awareness in Large Language Models: An Exploration through the Think-Solve-Verify Framework (2024)

As Large Language Models (LLMs) become increasingly influential in reasoning tasks, ensuring their trustworthiness and introspective self-awareness is critical. This research introduces the Think-Solve-Verify (TSV) framework, an innovative strategy tailored to explore LLMs' trustworthiness, introspective self-awareness, and collaborative reasoning. This method accentuates a model`s capability to construct introspective reasoning processes from answers and ensure their trustworthiness. The reasoning with TSV consistently performs at or near the top across the majority of datasets with a single interaction with LLM. Moreover, we refine the voting process of self-consistency within the Chain-of-Thought (CoT) approach, leading to notable accuracy enhancements. In our evaluations, this approach improved performance from 67.3{\%} to 72.8{\%} on the AQuA dataset. Furthermore, we delve into the model`s ability to explain the given answers, highlighting the significance of discerning genuine comprehension from mere guesswork.
Tug-of-War between Knowledge: Exploring and Resolving Knowledge Conflicts in Retrieval-Augmented Language Models (2024)

Retrieval-augmented language models (RALMs) have demonstrated significant potential in refining and expanding their internal memory by retrieving evidence from external sources. However, RALMs will inevitably encounter \textbf{knowledge conflicts} when integrating their internal memory with external sources. Knowledge conflicts can ensnare RALMs in a tug-of-war between knowledge, limiting their practical applicability. In this paper, we focus on exploring and resolving knowledge conflicts in RALMs. First, we present an evaluation framework for assessing knowledge conflicts across various dimensions. Then, we investigate the behavior and preference of RALMs from the following two perspectives: (1) Conflicts between internal memory and external sources: We find that stronger RALMs emerge with the Dunning-Kruger effect, persistently favoring their faulty internal memory even when correct evidence is provided. Besides, RALMs exhibit an availability bias towards common knowledge; (2) Conflicts between truthful, irrelevant and misleading evidence: We reveal that RALMs follow the principle of majority rule, leaning towards placing trust in evidence that appears more frequently. Moreover, we find that RALMs exhibit confirmation bias, and are more willing to choose evidence that is consistent with their internal memory. To solve the challenge of knowledge conflicts, we propose a method called Conflict-Disentangle Contrastive Decoding (CD2) to better calibrate the model`s confidence. Experimental results demonstrate that our CD2 can effectively resolve knowledge conflicts in RALMs.
TunArTTS: Tunisian Arabic Text-To-Speech Corpus (2024)

Being labeled as a low-resource language, the Tunisian dialect has no existing prior TTS research. In this paper, we present a speech corpus for Tunisian Arabic Text-to-Speech (TunArTTS) to initiate the development of end-to-end TTS systems for the Tunisian dialect. Our Speech corpus is extracted from an online English and Tunisian Arabic dictionary. We were able to extract a mono-speaker speech corpus of +3 hours of a male speaker sampled at 44100 kHz. The corpus is processed and manually diacritized. Furthermore, we develop various TTS systems based on two approaches: training from scratch and transfer learning. Both Tacotron2 and FastSpeech2 were used and evaluated using subjective and objective metrics. The experimental results show that our best results are obtained with the transfer learning from a pre-trained model on the English LJSpeech dataset. This model obtained a mean opinion score (MOS) of 3.88. TunArTTS will be publicly available for research purposes along with the baseline TTS system demo. Keywords: Tunisian Dialect, Text-To-Speech, Low-resource, Transfer Learning, TunArTTS
TweetTER: A Benchmark for Target Entity Retrieval on Twitter without Knowledge Bases (2024)

Entity linking is a well-established task in NLP consisting of associating entity mentions with entries in a knowledge base. Current models have demonstrated competitive performance in standard text settings. However, when it comes to noisy domains such as social media, certain challenges still persist. Typically, to evaluate entity linking on existing benchmarks, a comprehensive knowledge base is necessary and models are expected to possess an understanding of all the entities contained within the knowledge base. However, in practical scenarios where the objective is to retrieve sentences specifically related to a particular entity, strict adherence to a complete understanding of all entities in the knowledge base may not be necessary. To address this gap, we introduce TweetTER (Tweet Target Entity Retrieval), a novel benchmark that aims to bridge the challenges in entity linking. The distinguishing feature of this benchmark is its approach of re-framing entity linking as a binary entity retrieval task. This enables the evaluation of language models' performance without relying on a conventional knowledge base, providing a more practical and versatile evaluation framework for assessing the effectiveness of language models in entity retrieval tasks.