Evangelos Kanoulas


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Parameter-Efficient Abstractive Question Answering over Tables or Text
Vaishali Pal | Evangelos Kanoulas | Maarten Rijke
Proceedings of the Second DialDoc Workshop on Document-grounded Dialogue and Conversational Question Answering

A long-term ambition of information seeking QA systems is to reason over multi-modal contexts and generate natural answers to user queries. Today, memory intensive pre-trained language models are adapted to downstream tasks such as QA by fine-tuning the model on QA data in a specific modality like unstructured text or structured tables. To avoid training such memory-hungry models while utilizing a uniform architecture for each modality, parameter-efficient adapters add and train small task-specific bottle-neck layers between transformer layers. In this work, we study parameter-efficient abstractive QA in encoder-decoder models over structured tabular data and unstructured textual data using only 1.5% additional parameters for each modality. We also ablate over adapter layers in both encoder and decoder modules to study the efficiency-performance trade-off and demonstrate that reducing additional trainable parameters down to 0.7%-1.0% leads to comparable results. Our models out-perform current state-of-the-art models on tabular QA datasets such as Tablesum and FeTaQA, and achieve comparable performance on a textual QA dataset such as NarrativeQA using significantly less trainable parameters than fine-tuning.


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Robustness Evaluation of Entity Disambiguation Using Prior Probes: the Case of Entity Overshadowing
Vera Provatorova | Samarth Bhargav | Svitlana Vakulenko | Evangelos Kanoulas
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Entity disambiguation (ED) is the last step of entity linking (EL), when candidate entities are reranked according to the context they appear in. All datasets for training and evaluating models for EL consist of convenience samples, such as news articles and tweets, that propagate the prior probability bias of the entity distribution towards more frequently occurring entities. It was shown that the performance of the EL systems on such datasets is overestimated since it is possible to obtain higher accuracy scores by merely learning the prior. To provide a more adequate evaluation benchmark, we introduce the ShadowLink dataset, which includes 16K short text snippets annotated with entity mentions. We evaluate and report the performance of popular EL systems on the ShadowLink benchmark. The results show a considerable difference in accuracy between more and less common entities for all of the EL systems under evaluation, demonstrating the effect of prior probability bias and entity overshadowing.

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Combining Lexical and Dense Retrieval for Computationally Efficient Multi-hop Question Answering
Georgios Sidiropoulos | Nikos Voskarides | Svitlana Vakulenko | Evangelos Kanoulas
Proceedings of the Second Workshop on Simple and Efficient Natural Language Processing

In simple open-domain question answering (QA), dense retrieval has become one of the standard approaches for retrieving the relevant passages to infer an answer. Recently, dense retrieval also achieved state-of-the-art results in multi-hop QA, where aggregating information from multiple pieces of information and reasoning over them is required. Despite their success, dense retrieval methods are computationally intensive, requiring multiple GPUs to train. In this work, we introduce a hybrid (lexical and dense) retrieval approach that is highly competitive with the state-of-the-art dense retrieval models, while requiring substantially less computational resources. Additionally, we provide an in-depth evaluation of dense retrieval methods on limited computational resource settings, something that is missing from the current literature.


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WN-Salience: A Corpus of News Articles with Entity Salience Annotations
Chuan Wu | Evangelos Kanoulas | Maarten de Rijke | Wei Lu
Proceedings of the Twelfth Language Resources and Evaluation Conference

Entities can be found in various text genres, ranging from tweets and web pages to user queries submitted to web search engines. Existing research either considers all entities in the text equally important, or heuristics are used to measure their salience. We believe that a key reason for the relatively limited work on entity salience is the lack of appropriate datasets. To support research on entity salience, we present a new dataset, the WikiNews Salience dataset (WN-Salience), which can be used to benchmark tasks such as entity salience detection and salient entity linking. WN-Salience is built on top of Wikinews, a Wikimedia project whose mission is to present reliable news articles. Entities in Wikinews articles are identified by the authors of the articles and are linked to Wikinews categories when they are salient or to Wikipedia pages otherwise. The dataset is built automatically, and consists of approximately 7,000 news articles, and 90,000 in-text entity annotations. We compare the WN-Salience dataset against existing datasets on the task and analyze their differences. Furthermore, we conduct experiments on entity salience detection; the results demonstrate that WN-Salience is a challenging testbed that is complementary to existing ones.


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A distantly supervised dataset for automated data extraction from diagnostic studies
Christopher Norman | Mariska Leeflang | René Spijker | Evangelos Kanoulas | Aurélie Névéol
Proceedings of the 18th BioNLP Workshop and Shared Task

Systematic reviews are important in evidence based medicine, but are expensive to produce. Automating or semi-automating the data extraction of index test, target condition, and reference standard from articles has the potential to decrease the cost of conducting systematic reviews of diagnostic test accuracy, but relevant training data is not available. We create a distantly supervised dataset of approximately 90,000 sentences, and let two experts manually annotate a small subset of around 1,000 sentences for evaluation. We evaluate the performance of BioBERT and logistic regression for ranking the sentences, and compare the performance for distant and direct supervision. Our results suggest that distant supervision can work as well as, or better than direct supervision on this problem, and that distantly trained models can perform as well as, or better than human annotators.


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A light way to collect comparable corpora from the Web
Ahmet Aker | Evangelos Kanoulas | Robert Gaizauskas
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)

Statistical Machine Translation (SMT) relies on the availability of rich parallel corpora. However, in the case of under-resourced languages, parallel corpora are not readily available. To overcome this problem previous work has recognized the potential of using comparable corpora as training data. The process of obtaining such data usually involves (1) downloading a separate list of documents for each language, (2) matching the documents between two languages usually by comparing the document contents, and finally (3) extracting useful data for SMT from the matched document pairs. This process requires a large amount of time and resources since a huge volume of documents needs to be downloaded to increase the chances of finding good document pairs. In this work we aim to reduce the amount of time and resources spent for tasks 1 and 2. Instead of obtaining full documents we first obtain just titles along with some meta-data such as time and date of publication. Titles can be obtained through Web Search and RSS News feed collections so that download of the full documents is not needed. We show experimentally that titles can be used to approximate the comparison between documents using full document contents.