Malta

Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. The task typically relies on trigger detection (TD) -- identifying token spans in the text that evoke specific events. While the notion of triggers should ideally be universal across domains, domain transfer for TD from high- to low-resource domains results in significant performance drops. We address the problem of negative transfer in TD by coupling triggers between domains using subject-object relations obtained from a rule-based open information extraction (OIE) system. We demonstrate that OIE relations injected through multi-task training can act as mediators between triggers in different domains, enhancing zero- and few-shot TD domain transfer and reducing performance drops, in particular when transferring from a high-resource source domain (Wikipedia) to a low(er)-resource target domain (news). Additionally, we combine this improved transfer with masked language modeling on the target domain, observing further TD transfer gains. Finally, we demonstrate that the gains are robust to the choice of the OIE system.

EACL 2024

Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection

trigger detection

multi-task model

negative transfer

poster

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.

Scientific abstracts provide a concise summary of research findings, making them a valuable resource for extracting scientific arguments. In this study, we assess various unsupervised approaches for extracting arguments as aligned premise-conclusion pairs: semantic similarity, text perplexity, and mutual information. We aggregate structured abstracts from PubMed Central Open Access papers published in 2022 and evaluate the argument aligners in terms of the performance of language models that we fine-tune to generate the conclusions from the extracted premise given as input prompts. We find that mutual information outperforms the other measures on this task, suggesting that the reasoning process in scientific abstracts hinges mostly on linguistic constructs beyond simple textual similarity.

Evaluating Unsupervised Argument Aligners via Generation of Conclusions of Structured Scientific Abstracts

Many online content portals allow users to ask questions to supplement their understanding (e.g., of lectures). 
While information retrieval (IR) systems may provide answers for such user queries, they do not directly assist content creators---such as lecturers who want to improve their content---identify segments that caused a user to ask those questions.
We introduce the task of backtracing, in which systems retrieve the text segment that most likely caused a user query.
We formalize three real-world domains for which backtracing is important in improving content delivery and communication: understanding the cause of (a) student confusion in the Lecture domain, (b) reader curiosity in the News Article domain, and (c) user emotion in the Conversation domain.
We evaluate the zero-shot performance of popular information retrieval methods and language modeling methods, including bi-encoder, re-ranking and likelihood-based methods and ChatGPT.
While traditional IR systems retrieve semantically relevant information (e.g., details on ``projection matrices'' for a query ``does projecting multiple times still lead to the same point?''), they often miss the causally relevant context (e.g., the lecturer states ``projecting twice gets me the same answer as one projection''). 
Our results show that there is room for improvement on backtracing and it requires new retrieval approaches.
We hope our benchmark serves to improve future retrieval systems for backtracing, spawning systems that refine content generation and identify linguistic triggers influencing user queries.

Backtracing: Retrieving the Cause of the Query

Dense retrieval methods have demonstrated promising performance in multilingual information retrieval, where queries and documents can be in different languages. However, dense retrievers typically require a substantial amount of paired data, which poses even greater challenges in multilingual scenarios. This paper introduces $\textbf{UMR}$, an $\underline{U}$nsupervised $\underline{M}$ultilingual dense $\underline{R}$etriever trained without any paired data. Our approach leverages the sequence likelihood estimation capabilities of multilingual language models to acquire pseudo labels for training dense retrievers. We propose a two-stage framework which iteratively improves the performance of multilingual dense retrievers. Experimental results on two benchmark datasets show that UMR outperforms supervised baselines, showcasing the potential of training multilingual retrievers without paired data, thereby enhancing their practicality. All of our source code, data, and models are available: https://github.com/MiuLab/UMR

Unsupervised Multilingual Dense Retrieval via Generative Pseudo Labeling

In this paper, we propose a novel two-step technique for text classification using autoregressive Language Models (LM). In the first step, a set of perplexity and log-likelihood based numeric features are elicited from an LM for a text instance to be classified. Then, in the second step, a classifier based on these features is trained to predict the final label. The classifier used is usually a simple machine learning classifier like Support Vector Machine (SVM) or Logistic Regression (LR) and it is trained using a small set of training examples. We believe, our technique presents a whole new way of exploiting the available training instances, in addition to the existing ways like fine-tuning LMs or in-context learning. Our approach stands out by eliminating the need for parameter updates in LMs, as required in fine-tuning, and does not impose limitations on the number of training examples faced while building prompts for in-context learning. We evaluate our technique across 5 different datasets and compare with multiple competent baselines.

Why Generate When You Can Discriminate? A Novel Technique for Text Classification using Language Models

Argument Mining (AM) aims to uncover the argumentative structures within a text. Previous methods require several subtasks, such as span identification, component classification, and relation classification. 
Consequently, these methods need rule-based postprocessing to derive argumentative structures from the output of each subtask. 
This approach adds to the complexity of the model and expands the search space of the hyperparameters. 
To address this difficulty, we propose a simple yet strong method based on a text-to-text generation approach using a pretrained encoder-decoder language model. 
Our method simultaneously generates argumentatively annotated text for spans, components, and relations, eliminating the need for task-specific postprocessing and hyperparameter tuning. 
Furthermore, because it is a straightforward text-to-text generation method, we can easily adapt our approach to various types of argumentative structures.
Experimental results demonstrate the effectiveness of our method, as it achieves state-of-the-art performance on three different types of benchmark datasets: the Argument-annotated Essays Corpus~(AAEC), AbstRCT, and the Cornell eRulemaking Corpus (CDCP).

Argument Mining as a Text-to-Text Generation Task

Recent studies demonstrate that query expansions generated by large language models (LLMs) can considerably enhance information retrieval systems by generating hypothetical documents that answer the queries as expansions. However, challenges arise from misalignments between the expansions and the retrieval corpus, resulting in issues like hallucinations and outdated information due to the limited intrinsic knowledge of LLMs. Inspired by Pseudo Relevance Feedback (PRF), we introduce Corpus-Steered Query Expansion (CSQE) to promote the incorporation of knowledge embedded within the corpus. CSQE utilizes the relevance assessing capability of LLMs to systematically identify pivotal sentences in the initially-retrieved documents. These corpus-originated texts are subsequently used to expand the query together with LLM-knowledge empowered expansions, improving the relevance prediction between the query and the target documents. Extensive experiments reveal that CSQE exhibits strong performance without necessitating any training, especially with queries for which LLMs lack knowledge.

Corpus-Steered Query Expansion with Large Language Models

The output structure of database-like tables, consisting of values structured in horizontal rows and vertical columns identifiable by name, can cover a wide range of NLP tasks. Following this constatation, we propose a framework for text-to-table neural models applicable to problems such as extraction of line items, joint entity and relation extraction, or knowledge base population. The permutation-based decoder of our proposal is a generalized sequential method that comprehends information from all cells in the table. The training maximizes the expected log-likelihood for a table's content across all random permutations of the factorization order. During the content inference, we exploit the model's ability to generate cells in any order by searching over possible orderings to maximize the model's confidence and avoid substantial error accumulation, which other sequential models are prone to. Experiments demonstrate a high practical value of the framework, which establishes state-of-the-art results on several challenging datasets, outperforming previous solutions by up to $15\\%$.

STable: Table Generation Framework for Encoder-Decoder Models

The five human senses – vision, taste, smell, hearing, and touch – are key concepts that shape human perception of the world. The extraction of sensory references (i.e., expressions that evoke the presence of a sensory experience) in textual corpus is a challenge of high interest, with many applications in various areas. In this paper, we propose SENSE-LM, an information extraction system tailored for the discovery of sensory references in large collections of textual documents. Based on the novel idea of combining the strength of large language models and linguistic resources such as sensorimotor norms, it addresses the task of sensory information extraction at a coarse-grained (sentence binary classification) and fine-grained (sensory term extraction) level.
Our evaluation of SENSE-LM for two sensory functions, Olfaction and Audition, and comparison with state-of-the-art methods emphasize a significant leap forward in automating these complex tasks.

SENSE-LM : A Synergy between a Language Model and Sensorimotor Representations for Auditory and Olfactory Information Extraction

Using large language models (LMs) for query or document expansion can improve generalization in information retrieval. However, it is unknown whether these techniques are universally beneficial or only effective in specific settings, such as for particular retrieval models, dataset domains, or query types. To answer this, we conduct the first comprehensive analysis of LM-based expansion. We find that there exists a strong negative correlation between retriever performance and gains from expansion: expansion improves scores for weaker models, but generally harms stronger models. We show this trend holds across a set of eleven expansion techniques, twelve datasets with diverse distribution shifts, and twenty-four retrieval models. Through qualitative error analysis, we hypothesize that although expansions provide extra information (potentially improving recall), they add additional noise that makes it difficult to discern between the top relevant documents (thus introducing false positives). Our results suggest the following recipe: use expansions for weaker models or when the target dataset significantly differs from training corpus in format; otherwise, avoid expansions to keep the relevance signal clear.

When do Generative Query and Document Expansions Fail? A Comprehensive Study Across Methods, Retrievers, and Datasets

Passage retrieval is a crucial component of modern open-domain question answering (QA) systems, providing information for downstream QA components to generate accurate and transparent answers. In this study we focus on passage re-ranking, proposing a simple yet effective method, Joint Passage Re-ranking (JPR), that optimizes the mutual information between query and passage distributions, integrating both cross-encoders and generative models in the re-ranking process. Experimental results demonstrate that JPR outperforms conventional re-rankers and language model scorers in both open-domain QA retrieval settings and diverse retrieval benchmarks under zero-shot settings.

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Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection

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Evaluating Unsupervised Argument Aligners via Generation of Conclusions of Structured Scientific Abstracts

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