Croatia

Transformer-based models for question answering (QA) over tables and texts confront a ``long&#39;&#39; hybrid sequence over tabular and textual elements, causing long-range reasoning problems. To handle long-range reasoning, we extensively employ a fusion-in-decoder (FiD) and exponential moving average (EMA), proposing a \underline{M}oving \underline{A}verage Equipped \underline{F}usion-\underline{i}n-\underline{D}ecoder (\textbf{MAFiD}). With FiD as the backbone architecture, MAFiD combines various levels of reasoning: \textit{independent encoding} of homogeneous data and \textit{single-row} and \textit{multi-row heterogeneous reasoning}, using a \textit{gated cross attention layer} to effectively aggregate the three types of representations resulting from various reasonings. Experimental results on HybridQA indicate that MAFiD achieves state-of-the-art performance by increasing exact matching (EM) and F1 by $1.1$ and $1.7$, respectively, on the blind test set.

EACL 2023

MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data

fusion-in-decoder

long-range reasoning

question answering

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Message from the General Chair: 
*It is my great pleasure and honor to welcome you to the 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025), held in beautiful Vienna, Austria, from July 27 to August 1, 2025. ACL2025continues our field’s tradition of excellence in scholarship, innovation, and inclusivity, and I am deeply grateful to the many volunteers who have worked tirelessly to bring this event to life.* 
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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Transformer-based models for question answering (QA) over tables and texts confront a ``long'' hybrid sequence over tabular and textual elements, causing long-range reasoning problems. To handle long-range reasoning, we extensively employ a fusion-in-decoder (FiD) and exponential moving average (EMA), proposing a \underline{M}oving \underline{A}verage Equipped \underline{F}usion-\underline{i}n-\underline{D}ecoder (\textbf{MAFiD}). With FiD as the backbone architecture, MAFiD combines various levels of reasoning: \textit{independent encoding} of homogeneous data and \textit{single-row} and \textit{multi-row heterogeneous reasoning}, using a \textit{gated cross attention layer} to effectively aggregate the three types of representations resulting from various reasonings. Experimental results on HybridQA indicate that MAFiD achieves state-of-the-art performance by increasing exact matching (EM) and F1 by $1.1$ and $1.7$, respectively, on the blind test set.

findings / work in progress

### Welcome to the 17th 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 had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. 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.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

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.

Recent advances in the area of long document matching have primarily focused on using transformer-based models for long document encoding and matching. There are two primary challenges associated with these models. Firstly, the performance gain provided by transformer-based models comes at a steep cost – both in terms of the required training time and the resource (memory and energy) consumption. The second major limitation is their inability to handle more than a pre-defined input token length at a time. In this work, we empirically demonstrate the effectiveness of simple neural models (such as feed-forward networks, and CNNs) and simple embeddings (like GloVe, and Paragraph Vector) over transformer-based models on the task of document matching. We show that simple models outperform the more complex BERT-based models while taking significantly less training time, energy, and memory. The simple models are also more robust to variations in document length and text perturbations.

Transformer-based Models for Long-Form Document Matching: Challenges and Empirical Analysis

Pre-trained neural masked language models are often used for predicting a replacement token for a given sequence position, in a cloze-like task. However, this usage is restricted to predicting a single token, from a relatively small pre-trained vocabulary. Recent Sequence2Sequence pre-trained LMs like T5 do allow predicting multi-token completions, but are more expensive to train and run. We show that pre-trained masked language models can be adapted to produce multi-token completions, with only a modest addition to their parameter count. We propose two simple adaptation approaches, trading parameter counts for accuracy. The first method generates multi-token completions from a conditioned RNN. It has a very low parameter count and achieves competitive results. The second method is even simpler: it adds items corresponding to multi-token units to the output prediction matrix. While being higher in parameter count than the RNN method, it also surpasses current state-of-the-art multi-token completion models, including T5-3B, while being significantly more parameter efficient. We demonstrate that our approach is flexible to different vocabularies and domains and can effectively leverage existing pre-trained models available in different domains. Finally, a human evaluation further validates our results and shows that our solution regularly provides valid completions, as well as reasonable correctness for factual-sentence completions.

Simple and Effective Multi-Token Completion from Masked Language Models

Activation functions can have a significant impact on reducing the topological complexity of input data and therefore, improving a model's performance. However, the choice of activation functions is seldom discussed or explored in Transformer-based language models. As a common practice, commonly used activation functions like Gaussian Error Linear Unit (GELU) are chosen beforehand and then remain fixed from pre-training to fine-tuning. In this paper, we investigate the impact of activation functions on Transformer-based models by utilizing rational activation functions (RAFs). In contrast to fixed activation functions (FAF), RAFs are capable of learning the optimal activation functions from data. Our experiments show that the RAF-based Transformer model (RAFT) achieves a better performance than its FAF-based counterpart (\baseline). For instance, we find that RAFT outperforms \baseline on the GLUE benchmark by 5.71 points when using only 100 training examples and by 2.05 points on SQuAD with all available data. Analyzing the shapes of the learned RAFs further unveils that they vary across different layers and different tasks; opening a promising way to better analyze and understand large, pre-trained language models.

Transformers with Learnable Activation Functions

Due to privacy or commercial constraints, large pre-trained language models (PLMs) are often offered as black-box APIs. Fine-tuning such models to downstream tasks is challenging because one can neither access the model's internal representations nor propagate gradients through it. This paper addresses these challenges by developing techniques for adapting PLMs with only API access. Building on recent work on soft prompt tuning, we develop methods to tune the soft prompts without requiring gradient computation. Further, we develop extensions that in addition to not requiring gradients also do not need to access any internal representation of the PLM beyond the input embeddings. Moreover, instead of learning a single prompt, our methods learn a distribution over prompts allowing us to quantify predictive uncertainty. Ours is the first work to consider uncertainty in prompts when only having API access to the PLM. Finally, through extensive experiments, we carefully vet the proposed methods and find them competitive with (and sometimes even improving on) gradient-based approaches with full access to the PLM.

Reliable Gradient-free and Likelihood-free Prompt Tuning

Although many large-scale knowledge bases (KBs) claim to contain multilingual information, their support for many non-English languages is often incomplete. This incompleteness gives birth to the task of cross-lingual question answering over knowledge base (xKBQA), which aims to answer questions in languages different from that of the provided KB. One of the major challenges facing xKBQA is the high cost of data annotation, leading to limited resources available for further exploration. Another challenge is mapping KB schemas and natural language expressions in the questions under cross-lingual settings. In this paper, we propose a novel approach for xKBQA in a reading comprehension paradigm. We convert KB subgraphs into passages to narrow the gap between KB schemas and questions, which enables our model to benefit from recent advances in multilingual pre-trained language models (MPLMs) and cross-lingual machine reading comprehension (xMRC). Specifically, we use MPLMs, with considerable knowledge of cross-lingual mappings, for cross-lingual reading comprehension. Existing high-quality xMRC datasets can be further utilized to finetune our model, greatly alleviating the data scarcity issue in xKBQA. Extensive experiments on two xKBQA datasets in 12 languages show that our approach outperforms various baselines and achieves strong few-shot and zero-shot performance. Our dataset and code are released for further research.

Cross-Lingual Question Answering over Knowledge Base as Reading Comprehension

Visual question answering (VQA) is one of the crucial vision-and-language tasks. Yet, existing VQA research has mostly focused on the English language, due to a lack of suitable evaluation resources. Previous work on cross-lingual VQA has reported poor zero-shot transfer performance of current multilingual multimodal Transformers with large gaps to monolingual performance, without any deeper analysis. In this work, we delve deeper into the different aspects of cross-lingual VQA, aiming to understand the impact of 1) modeling methods and choices, including architecture, inductive bias, fine-tuning; 2) learning biases: including question types and modality biases in cross-lingual setups. The key results of our analysis are: 1. We show that simple modifications to the standard training setup can substantially reduce the transfer gap to monolingual English performance, yielding +10 accuracy points over existing methods. 2. We analyze cross-lingual VQA across different question types of varying complexity for different multilingual multimodal Transformers, and identify question types that are the most difficult to improve on. 3. We provide an analysis of modality biases present in training data and models, revealing why zero-shot performance gaps remain for certain question types and languages.

Delving Deeper into Cross-lingual Visual Question Answering

Assessing the quality of an argument is a complex, highly subjective task, influenced by heterogeneous factors (e.g., prior beliefs of the annotators, topic, domain, and application), and crucial for its impact in downstream tasks (e.g., argument retrieval or generation). Both the Argument Mining and the Social Science community have devoted plenty of attention to it, resulting in a wide variety of argument quality dimensions and a large number of annotated resources.
This work aims at a better understanding of how the different aspects of argument quality relate to each other from a practical point of view. We employ adapter-fusion (Pfeiffer et al., 2021) as a multi-task learning framework which a) can improve the prediction of individual quality dimensions by injecting knowledge about related dimensions b) is efficient and modular and c) can serve as an analysis tool to investigate relations between different dimensions. We conduct experiments on 6 datasets and 20 quality dimensions. We find that the majority of the dimensions can be learned as a weighted combination of other quality aspects, and that for 8 dimensions adapter fusion improves quality prediction. Last, we show the benefits of this approach by improving the performance in an extrinsic, out-of-domain task: prediction of moderator interventions in a deliberative forum.

Bridging Argument Quality and Deliberative Quality Annotations with Adapters

Pretraining large language models has resulted in tremendous performance improvement for many natural language processing (NLP) tasks. While for non-domain specific tasks, such models can be used directly, a common strategy to achieve better performance for specific domains involves pretraining these language models over domain specific data using objectives like Masked Language Modelling (MLM), Autoregressive Language Modelling, etc. While such pretraining addresses the change in vocabulary and style of language for the domain, it is otherwise a domain agnostic approach. In this work, we investigate the effect of incorporating pretraining objectives that explicitly tries to exploit the domain specific language characteristics in addition to such MLM based pretraining. Particularly, we examine two distinct characteristics associated with the legal domain and propose pretraining objectives modelling these characteristics. The proposed objectives target improvement of token-level feature representation, as well as aim to incorporate sentence level semantics. We demonstrate superiority in the performance of the models pretrained using our objectives against those trained using domain-agnostic objectives over several legal downstream tasks.

Exploiting Language Characteristics for Legal Domain-Specific Language Model Pretraining

Determining the role of event arguments is a crucial subtask of event extraction. Most previous supervised models leverage costly annotations, which is not practical for open-domain applications. In this work, we propose to use global constraints with prompting to effectively tackles event argument classification without any annotation and task-specific training. Specifically, given an event and its associated passage, the model first creates several new passages by prefix prompts and cloze prompts, where prefix prompts indicate event type and trigger span, and cloze prompts connect each candidate role with the target argument span. Then, a pre-trained language model scores the new passages, making the initial prediction. Our novel prompt templates can easily adapt to all events and argument types without manual effort. Next, the model regularizes the prediction by global constraints exploiting cross-task, cross-argument, and cross-event relations. Extensive experiments demonstrate our model's effectiveness: it outperforms the best zero-shot baselines by 12.5% and 10.9% F1 on ACE and ERE with given argument spans and by 4.3% and 3.3% F1, respectively, without given argument spans. We have made our code publicly available.

Global Constraints with Prompting for Zero-Shot Event Argument Classification

Driven by encouraging results on a wide range of tasks, the field of NLP is experiencing an accelerated race to develop bigger language models. 
This race for bigger models has also underscored the need to continue the pursuit of practical distillation approaches that can leverage the knowledge acquired by these big models in a compute-efficient manner. Having this goal in mind, we build on recent work to propose a hallucination-free framework for sequence tagging that is especially suited for distillation. We show empirical results of new state-of-the-art performance across multiple sequence labelling datasets and validate the usefulness of this framework for distilling a large model in a few-shot learning scenario.

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