198 research outputs found
Bringing order into the realm of Transformer-based language models for artificial intelligence and law
Transformer-based language models (TLMs) have widely been recognized to be a
cutting-edge technology for the successful development of deep-learning-based
solutions to problems and applications that require natural language processing
and understanding. Like for other textual domains, TLMs have indeed pushed the
state-of-the-art of AI approaches for many tasks of interest in the legal
domain. Despite the first Transformer model being proposed about six years ago,
there has been a rapid progress of this technology at an unprecedented rate,
whereby BERT and related models represent a major reference, also in the legal
domain. This article provides the first systematic overview of TLM-based
methods for AI-driven problems and tasks in the legal sphere. A major goal is
to highlight research advances in this field so as to understand, on the one
hand, how the Transformers have contributed to the success of AI in supporting
legal processes, and on the other hand, what are the current limitations and
opportunities for further research development.Comment: Please refer to the published version: Greco, C.M., Tagarelli, A.
(2023) Bringing order into the realm of Transformer-based language models for
artificial intelligence and law. Artif Intell Law, Springer Nature. November
2023. https://doi.org/10.1007/s10506-023-09374-
Tab this Folder of Documents: Page Stream Segmentation of Business Documents
In the midst of digital transformation, automatically understanding
the structure and composition of scanned documents is important in
order to allow correct indexing, archiving, and processing. In many
organizations, different types of documents are usually scanned
together in folders, so it is essential to automate the task of segmenting the folders into documents which then proceed to further
analysis tailored to specific document types. This task is known
as Page Stream Segmentation (PSS). In this paper, we propose a
deep learning solution to solve the task of determining whether
or not a page is a breaking-point given a sequence of scanned
pages (a folder) as input. We also provide a dataset called TABME
(TAB this folder of docuMEnts) generated specifically for this task.
Our proposed architecture combines LayoutLM and ResNet to exploit both textual and visual features of the document pages and
achieves an F1 score of 0.953. The dataset and code used to run the
experiments in this paper are available at the following web link:
https://github.com/aldolipani/TABME
Corpus for Automatic Structuring of Legal Documents
In populous countries, pending legal cases have been growing exponentially.
There is a need for developing techniques for processing and organizing legal
documents. In this paper, we introduce a new corpus for structuring legal
documents. In particular, we introduce a corpus of legal judgment documents in
English that are segmented into topical and coherent parts. Each of these parts
is annotated with a label coming from a list of pre-defined Rhetorical Roles.
We develop baseline models for automatically predicting rhetorical roles in a
legal document based on the annotated corpus. Further, we show the application
of rhetorical roles to improve performance on the tasks of summarization and
legal judgment prediction. We release the corpus and baseline model code along
with the paper.Comment: Accepted at LREC 2022, 10 Pages (8 page main paper + 2 page
references
Document-Level Multi-Event Extraction with Event Proxy Nodes and Hausdorff Distance Minimization
Document-level multi-event extraction aims to extract the structural
information from a given document automatically. Most recent approaches usually
involve two steps: (1) modeling entity interactions; (2) decoding entity
interactions into events. However, such approaches ignore a global view of
inter-dependency of multiple events. Moreover, an event is decoded by
iteratively merging its related entities as arguments, which might suffer from
error propagation and is computationally inefficient. In this paper, we propose
an alternative approach for document-level multi-event extraction with event
proxy nodes and Hausdorff distance minimization. The event proxy nodes,
representing pseudo-events, are able to build connections with other event
proxy nodes, essentially capturing global information. The Hausdorff distance
makes it possible to compare the similarity between the set of predicted events
and the set of ground-truth events. By directly minimizing Hausdorff distance,
the model is trained towards the global optimum directly, which improves
performance and reduces training time. Experimental results show that our model
outperforms previous state-of-the-art method in F1-score on two datasets with
only a fraction of training time
Comparative study on Judgment Text Classification for Transformer Based Models
This work involves the usage of various NLP models to predict the winner of a
particular judgment by the means of text extraction and summarization from a
judgment document. These documents are useful when it comes to legal
proceedings. One such advantage is that these can be used for citations and
precedence reference in Lawsuits and cases which makes a strong argument for
their case by the ones using it. When it comes to precedence, it is necessary
to refer to an ample number of documents in order to collect legal points with
respect to the case. However, reviewing these documents takes a long time to
analyze due to the complex word structure and the size of the document. This
work involves the comparative study of 6 different self-attention-based
transformer models and how they perform when they are being tweaked in 4
different activation functions. These models which are trained with 200
judgement contexts and their results are being judged based on different
benchmark parameters. These models finally have a confidence level up to 99%
while predicting the judgment. This can be used to get a particular judgment
document without spending too much time searching relevant cases and reading
them completely.Comment: 28 pages with 9 figure
Generative Interpretation
We introduce generative interpretation, a new approach to estimating
contractual meaning using large language models. As AI triumphalism is the
order of the day, we proceed by way of grounded case studies, each illustrating
the capabilities of these novel tools in distinct ways. Taking well-known
contracts opinions, and sourcing the actual agreements that they adjudicated,
we show that AI models can help factfinders ascertain ordinary meaning in
context, quantify ambiguity, and fill gaps in parties' agreements. We also
illustrate how models can calculate the probative value of individual pieces of
extrinsic evidence. After offering best practices for the use of these models
given their limitations, we consider their implications for judicial practice
and contract theory. Using LLMs permits courts to estimate what the parties
intended cheaply and accurately, and as such generative interpretation
unsettles the current interpretative stalemate. Their use responds to
efficiency-minded textualists and justice-oriented contextualists, who argue
about whether parties will prefer cost and certainty or accuracy and fairness.
Parties--and courts--would prefer a middle path, in which adjudicators strive
to predict what the contract really meant, admitting just enough context to
approximate reality while avoiding unguided and biased assimilation of
evidence. As generative interpretation offers this possibility, we argue it can
become the new workhorse of contractual interpretation
Exploring acceptance of autonomous vehicle policies using KeyBERT and SNA: Targeting engineering students
This study aims to explore user acceptance of Autonomous Vehicle (AV)
policies with improved text-mining methods. Recently, South Korean policymakers
have viewed Autonomous Driving Car (ADC) and Autonomous Driving Robot (ADR) as
next-generation means of transportation that will reduce the cost of
transporting passengers and goods. They support the construction of V2I and V2V
communication infrastructures for ADC and recognize that ADR is equivalent to
pedestrians to promote its deployment into sidewalks. To fill the gap where
end-user acceptance of these policies is not well considered, this study
applied two text-mining methods to the comments of graduate students in the
fields of Industrial, Mechanical, and Electronics-Electrical-Computer. One is
the Co-occurrence Network Analysis (CNA) based on TF-IWF and Dice coefficient,
and the other is the Contextual Semantic Network Analysis (C-SNA) based on both
KeyBERT, which extracts keywords that contextually represent the comments, and
double cosine similarity. The reason for comparing these approaches is to
balance interest not only in the implications for the AV policies but also in
the need to apply quality text mining to this research domain. Significantly,
the limitation of frequency-based text mining, which does not reflect textual
context, and the trade-off of adjusting thresholds in Semantic Network Analysis
(SNA) were considered. As the results of comparing the two approaches, the
C-SNA provided the information necessary to understand users' voices using
fewer nodes and features than the CNA. The users who pre-emptively understood
the AV policies based on their engineering literacy and the given texts
revealed potential risks of the AV accident policies. This study adds
suggestions to manage these risks to support the successful deployment of AVs
on public roads.Comment: 29 pages with 11 figure
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