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InterroLang: Exploring NLP Models and Datasets through Dialogue-based Explanations

While recently developed NLP explainability methods let us open the black box in various ways (Madsen et al., 2022), a missing ingredient in this endeavor is an interactive tool offering a conversational interface. Such a dialogue system can help …

Factuality Detection using Machine Translation - a Use Case for German Clinical Text

Factuality can play an important role when automatically processing clinical text, as it makes a difference if particular symptoms are explicitly not present, possibly present, not mentioned, or affirmed. In most cases, a sufficient number of …

Inseq: An Interpretability Toolkit for Sequence Generation Models

Past work in natural language processing interpretability focused mainly on popular classification tasks while largely overlooking generation settings, partly due to a lack of dedicated tools. In this work, we introduce Inseq, a Python library to …

Neural Machine Translation Methods for Translating Text to Sign Language Glosses

State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language (SL) glosses. In our experiments, we improve the performance of the transformer-based models via (1) data …

Saliency Map Verbalization: Comparing Feature Importance Representations from Model-free and Instruction-based Methods

Saliency maps can explain a neural model's predictions by identifying important input features. They are difficult to interpret for laypeople, especially for instances with many features. In order to make them more accessible, we formalize the …

Efficient Language Model Training through Cross-Lingual and Progressive Transfer Learning

Most Transformer language models are primarily pretrained on English text, limiting their use for other languages. As the model sizes grow, the performance gap between English and other languages with fewer compute and data resources increases even …

MultiTACRED: A Multilingual Version of the TAC Relation Extraction Dataset

Relation extraction (RE) is a fundamental task in information extraction, whose extension to multilingual settings has been hindered by the lack of supervised resources comparable in size to large English datasets such as TACRED (Zhang et al., 2017). …

VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets

The anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. Consequently, illegal markets and their connections are challenging to uncover on the Darknet. To identify …

Findings of the WMT 2022 Biomedical Translation Shared Task: Monolingual Clinical Case Reports

In the seventh edition of the WMT Biomedical Task, we addressed a total of seven languagepairs, namely English/German, English/French, English/Spanish, English/Portuguese, English/Chinese, English/Russian, English/Italian. This year{'}s test sets …

Multilingual Relation Classification via Efficient and Effective Prompting

Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual settings, applying prompt-based methods in multilingual scenarios has been …