Search Results for author:
Found 40 papers, 19 papers with code
MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
We use the dataset as a testbed for zero-shot cross-lingual transfer, where we exploit annotated training documents in one language (source) to classify documents in another language (target).
Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs
Instruction-finetuned Large Language Models inherit clear political leanings that have been shown to influence downstream task performance.
On the Interplay between Fairness and Explainability
In order to build reliable and trustworthy NLP applications, models need to be both fair across different demographics and explainable.
Rather a Nurse than a Physician -- Contrastive Explanations under Investigation
A cross-comparison between model-based rationales and human annotations, both in contrastive and non-contrastive settings, yields a high agreement between the two settings for models as well as for humans.
Regulation and NLP (RegNLP): Taming Large Language Models
Regulation studies are a rich source of knowledge on how to systematically deal with risk and uncertainty, as well as with scientific evidence, to evaluate and compare regulatory options.
SCALE: Scaling up the Complexity for Advanced Language Model Evaluation
In this paper, we introduce a novel NLP benchmark that poses challenges to current LLMs across four key dimensions: processing long documents (up to 50K tokens), utilizing domain specific knowledge (embodied in legal texts), multilingual understanding (covering five languages), and multitasking (comprising legal document to document Information Retrieval, Court View Generation, Leading Decision Summarization, Citation Extraction, and eight challenging Text Classification tasks).
PokemonChat: Auditing ChatGPT for Pokémon Universe Knowledge
We then evaluate its ability to acquire new knowledge and include it in its reasoning process.
MultiLegalPile: A 689GB Multilingual Legal Corpus
Large, high-quality datasets are crucial for training Large Language Models (LLMs).
Efficient Document Embeddings via Self-Contrastive Bregman Divergence Learning
Learning quality document embeddings is a fundamental problem in natural language processing (NLP), information retrieval (IR), recommendation systems, and search engines.
Retrieval-augmented Multi-label Text Classification
Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution.
Multi Label Text Classification
Multi-Label Text Classification
+2
LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development
To this end, we release a multinational English legal corpus (LeXFiles) and a legal knowledge probing benchmark (LegalLAMA) to facilitate training and detailed analysis of legal-oriented PLMs.
An Exploration of Encoder-Decoder Approaches to Multi-Label Classification for Legal and Biomedical Text
Standard methods for multi-label text classification largely rely on encoder-only pre-trained language models, whereas encoder-decoder models have proven more effective in other classification tasks.
Multi-Label Classification
Multi Label Text Classification
+2
ChatGPT may Pass the Bar Exam soon, but has a Long Way to Go for the LexGLUE benchmark
Following the hype around OpenAI's ChatGPT conversational agent, the last straw in the recent development of Large Language Models (LLMs) that demonstrate emergent unprecedented zero-shot capabilities, we audit the latest OpenAI's GPT-3. 5 model, `gpt-3. 5-turbo', the first available ChatGPT model, in the LexGLUE benchmark in a zero-shot fashion providing examples in a templated instruction-following format.
LEXTREME: A Multi-Lingual and Multi-Task Benchmark for the Legal Domain
To provide a fair comparison, we propose two aggregate scores, one based on the datasets and one on the languages.
Processing Long Legal Documents with Pre-trained Transformers: Modding LegalBERT and Longformer
Even sparse-attention models, such as Longformer and BigBird, which increase the maximum input length to 4, 096 sub-words, severely truncate texts in three of the six datasets of LexGLUE.
Legal-Tech Open Diaries: Lesson learned on how to develop and deploy light-weight models in the era of humongous Language Models
In the era of billion-parameter-sized Language Models (LMs), start-ups have to follow trends and adapt their technology accordingly.
An Exploration of Hierarchical Attention Transformers for Efficient Long Document Classification
Non-hierarchical sparse attention Transformer-based models, such as Longformer and Big Bird, are popular approaches to working with long documents.
An Empirical Study on Cross-X Transfer for Legal Judgment Prediction
We find that in both settings (legal areas, origin regions), models trained across all groups perform overall better, while they also have improved results in the worst-case scenarios.
Realistic Zero-Shot Cross-Lingual Transfer in Legal Topic Classification
We consider zero-shot cross-lingual transfer in legal topic classification using the recent MultiEURLEX dataset.
Revisiting Transformer-based Models for Long Document Classification
The recent literature in text classification is biased towards short text sequences (e. g., sentences or paragraphs).
Challenges and Strategies in Cross-Cultural NLP
Various efforts in the Natural Language Processing (NLP) community have been made to accommodate linguistic diversity and serve speakers of many different languages.
Improved Multi-label Classification under Temporal Concept Drift: Rethinking Group-Robust Algorithms in a Label-Wise Setting
In document classification for, e. g., legal and biomedical text, we often deal with hundreds of classes, including very infrequent ones, as well as temporal concept drift caused by the influence of real world events, e. g., policy changes, conflicts, or pandemics.
FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained language models and the techniques used to fine-tune them for downstream tasks.
FiNER: Financial Numeric Entity Recognition for XBRL Tagging
We, therefore, introduce XBRL tagging as a new entity extraction task for the financial domain and release FiNER-139, a dataset of 1. 1M sentences with gold XBRL tags.
LexGLUE: A Benchmark Dataset for Legal Language Understanding in English
Laws and their interpretations, legal arguments and agreements\ are typically expressed in writing, leading to the production of vast corpora of legal text.
Ranked #1 on
Natural Language Understanding
on LexGLUE
Swiss-Judgment-Prediction: A Multilingual Legal Judgment Prediction Benchmark
We evaluate state-of-the-art BERT-based methods including two variants of BERT that overcome the BERT input (text) length limitation (up to 512 tokens).
Multi-granular Legal Topic Classification on Greek Legislation
In this work, we study the task of classifying legal texts written in the Greek language.
MultiEURLEX -- A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer
We use the dataset as a testbed for zero-shot cross-lingual transfer, where we exploit annotated training documents in one language (source) to classify documents in another language (target).
Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases
We also release a new dataset comprising European Court of Human Rights cases, including annotations for paragraph-level rationales.
Regulatory Compliance through Doc2Doc Information Retrieval: A case study in EU/UK legislation where text similarity has limitations
Major scandals in corporate history have urged the need for regulatory compliance, where organizations need to ensure that their controls (processes) comply with relevant laws, regulations, and policies.
Neural Contract Element Extraction Revisited: Letters from Sesame Street
Morpho-syntactic features in the form of POS tag and token shape embeddings, as well as context-aware ELMO embeddings do not improve performance.
Layer-wise Guided Training for BERT: Learning Incrementally Refined Document Representations
Although BERT is widely used by the NLP community, little is known about its inner workings.
LEGAL-BERT: The Muppets straight out of Law School
Thus we propose a systematic investigation of the available strategies when applying BERT in specialised domains.
An Empirical Study on Large-Scale Multi-Label Text Classification Including Few and Zero-Shot Labels
Furthermore, we show that Transformer-based approaches outperform the state-of-the-art in two of the datasets, and we propose a new state-of-the-art method which combines BERT with LWANs.
Multi-Label Classification
Multi Label Text Classification
+5
GREEK-BERT: The Greeks visiting Sesame Street
We expect these resources to boost NLP research and applications for modern Greek.
Large-Scale Multi-Label Text Classification on EU Legislation
We consider Large-Scale Multi-Label Text Classification (LMTC) in the legal domain.
Ranked #1 on
Multi-Label Text Classification
on EUR-Lex
Neural Legal Judgment Prediction in English
Legal judgment prediction is the task of automatically predicting the outcome of a court case, given a text describing the case's facts.
Ranked #1 on
Binary text classification
on ECHR Non-Anonymized
Extreme Multi-Label Legal Text Classification: A case study in EU Legislation
We consider the task of Extreme Multi-Label Text Classification (XMTC) in the legal domain.
Obligation and Prohibition Extraction Using Hierarchical RNNs
We consider the task of detecting contractual obligations and prohibitions.
