United Arab Emirates

Generating domain-specific content such as legal clauses based on minimal user-provided information can be of significant benefit in automating legal contract generation. In this paper, we propose a controllable graph-based mechanism that can generate legal clauses using only the topic or type of the legal clauses. Our pipeline consists of two stages involving a graph-based planner followed by a clause generator. The planner outlines the content of a legal clause as a sequence of keywords in the order of generic to more specific clause information based on the input topic using a controllable graph-based mechanism. The generation stage takes in a given plan and generates a clause. The pipeline consists of a graph-based planner followed by text generation. We illustrate the effectiveness of our proposed two-stage approach on a broad set of clause topics in contracts.

EMNLP 2022

Graph-based Keyword Planning for Legal Clause Generation from Topics

clause generation

graph-based planning

legal clauses

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible. The experience for on-site participants would closely approximate a normal pre-COVID *ACL conference, with 5-6 thematically organized parallel sessions and live Q/A and interactive discussion immediately after the talks. Presentations by virtual participants will be equitably interleaved with those of on-site participants, projected on the auditorium screens as if on-site, and also followed immediately by live Q/A and interactive discussion at a time during reasonable waking hours for the virtual presenter. For all participants, on-site and virtual, who are unable to attend a session due to either time-zone issues or because they are participating in another session live, talk recordings and slides will be available online at a minimum after the live presentation (and in many cases before as well), and questions may be submitted in advance on session-specific discussion boards and answered live in session with the usual visual aids if desired.

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.

workshop paper

## Welcome to EMNLP 2022!
I am delighted to welcome you to EMNLP 2022!  I believe this conference has been complicated beyond any precedent.  Over the past year, it’s been thrilling to see the organization team approach each new puzzle with creativity and enthusiasm.  We hope that those participating in Abu Dhabi as well as those joining remotely will leave the conference feeling newly inspired by the program and newly connected to our ever-growing community.  Following EMNLP 2021 and major NLP conferences since, EMNLP 2022 is “hybrid,” serving both virtual and in-person participants.

Our key innovations for EMNLP 2022 include:

* EMNLP 2022 is “hybrid” in a second sense, as well:  we allowed both direct and rolling review paper submissions, building on the pilot experiment of EMNLP 2021, which considered a small number of ARR submissions. 
* Familiar from NAACL but new to EMNLP, we’ve added an industry track.
* During the conference, “portals” will link virtual poster sessions to in-person conference participants during poster sessions each day.
* The first ACL-family conference in the United Arab Emirates.

<br>*Message from Noah A. Smith, University of Washington and Allen Institute for AI, Seattle, Washington, USA*<br>
***EMNLP 2022 General Chair***
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Welcome!
EMNLP 2022 will take place in Abu Dhabi from December 7th to December 11th, 2022. And it will be held in hybrid mode, both online and offline.

Legal documents such as contracts contain complex and domain-specific jargons, long and nested sentences, and often present with several details that may be difficult to understand for laypeople without domain expertise. In this paper, we explore the problem of text simplification (TS) in legal domain. The main challenge to this is the lack of availability of complex-simple parallel datasets for the legal domain. We investigate some of the existing datasets, methods, and metrics in the TS literature for simplifying legal texts, and perform human evaluation to analyze the gaps.\textbackslash{}footnote\{The model outputs and human ratings are available at \textbackslash{}url\{https://bit.ly/3U3ddIl\}.\} We present some of the challenges involved, and outline a few open questions that need to be addressed for future research in this direction.

Text Simplification for Legal Domain: \{I\}nsights and Challenges

Named Entity Recognition (NER) is a well-explored area from Information Retrieval and Natural Language Processing with an extensive research community. Despite that, few languages, such as English and German, are well-resourced, whereas many other languages, such as Romanian, have scarce resources, especially in domain-specific applications. In this work, we address the NER problem in the legal domain from both Romanian and German languages and evaluate the performance of our proposed method based on domain adaptation. We employ multi-task learning to jointly train a neural network on two legal and general domains and perform adaptation among them. The results show that domain adaptation increase performances by a small amount, under 1\%, while considerable improvements are in the recall metric.

Legal Named Entity Recognition with Multi-Task Domain Adaptation

Though many algorithms can be used to automatically summarize legal case decisions, most fail to incorporate domain knowledge about how important sentences in a legal decision relate to a representation of its document structure. For example, analysis of a legal case sum- marization dataset demonstrates that sentences serving different types of argumentative roles in the decision appear in different sections of the document. In this work, we propose an unsupervised graph-based ranking model that uses a reweighting algorithm to exploit properties of the document structure of legal case decisions. We also explore the impact of using different methods to compute the document structure. Results on the Canadian Legal Case Law dataset show that our proposed method outperforms several strong baselines.

Computing and Exploiting Document Structure to Improve Unsupervised Extractive Summarization of Legal Case Decisions

Active Learning (AL) is a powerful tool for learning with less labeled data, in particular, for specialized domains, like legal documents, where unlabeled data is abundant, but the annotation requires domain expertise and is thus expensive. Recent works have shown the effectiveness of AL strategies for pre-trained language models. However, most AL strategies require a set of labeled samples to start with, which is expensive to acquire. In addition, pre-trained language models have been shown unstable during fine-tuning with small datasets, and their embeddings are not semantically meaningful. In this work, we propose a pipeline for effectively using active learning with pre-trained language models in the legal domain. To this end, we leverage the available \textbackslash{}textit\{unlabeled\} data in three phases. First, we continue pre-training the model to adapt it to the downstream task. Second, we use knowledge distillation to guide the model's embeddings to a semantically meaningful space. Finally, we propose a simple, yet effective, strategy to find the initial set of labeled samples with fewer actions compared to existing methods. Our experiments on Contract-NLI, adapted to the classification task, and LEDGAR benchmarks show that our approach outperforms standard AL strategies, and is more efficient. Furthermore, our pipeline reaches comparable results to the fully-supervised approach with a small performance gap, and dramatically reduced annotation cost. Code and the adapted data will be made available.

An Efficient Active Learning Pipeline for Legal Text Classification

Abstract Meaning Representation (AMR) has been shown to be useful for many downstream tasks. In this work, we explore the use of AMR for legal and logical reasoning. Specifically, we investigate if AMR can help capture logical relationships on multiple choice question answering (MCQA) tasks. We propose neural architectures that utilize linearised AMR graphs in combination with pre-trained language models. While these models are not able to outperform text-only baselines, they correctly solve different instances than the text models, suggesting complementary abilities. Error analysis further reveals that AMR parsing quality is the most prominent challenge, especially regarding inputs with multiple sentences. We conduct a theoretical analysis of how logical relations are represented in AMR and conclude it might be helpful in some logical statements but not for others.

Can AMR Assist Legal and Logical Reasoning?

In this work,  we focus on sentence splitting, a subfield of text simplification, primarily motivated by an unproven idea that if you divide a sentence into pieces, it should become easier to understand.   Our primary goal in this paper is to determine whether this is true. In particular, we ask, does it matter whether we break a sentence into two or three?  We report on our findings based on Amazon Mechanical Turk. More specifically, we introduce a Bayesian modeling framework to further investigate to what degree a particular way of splitting the complex sentence affects readability, along with a number of other parameters adopted from diverse perspectives, including clinical linguistics, and cognitive linguistics. The Bayesian modeling experiment provides clear evidence that bisecting the sentence leads to enhanced readability to a degree greater than when we create simplification by trisection.

The Fewer Splits are Better: Deconstructing Readability in Sentence Splitting

We propose a method of parallel corpus filtering for Japanese text simplification. The parallel corpus for this task contains some redundant wording. In this study, we first identify the type and size of noisy sentence pairs in the Japanese text simplification corpus. We then propose a method of parallel corpus filtering to remove each type of noisy sentence pair. Experimental results show that filtering the training parallel corpus with the proposed method improves simplification performance.

Parallel Corpus Filtering for Japanese Text Simplification

Automatic text simplification can help patients to better understand their own clinical notes. A major hurdle for the development of clinical text simplification methods is the lack of high quality resources. We report ongoing efforts in creating a parallel dataset of professionally simplified clinical notes. Currently, this corpus consists of 851 document-level simplifications of German pathology reports. We highlight characteristics of this dataset and establish first baselines for paragraph-level simplification.

Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset

Automatic text simplification aims to reduce the linguistic complexity of a text in order to make it easier to understand and more accessible. However, simplified texts are consumed by a diverse array of target audiences and what might be appropriately simplified for one group of readers may differ considerably for another. In this work we investigate a novel formulation of sentence simplification as paraphrasing with controlled decoding. This approach aims to alleviate the major burden of relying on large amounts of in-domain parallel training data, while at the same time allowing for modular and adaptive simplification. According to automatic metrics, our approach performs competitively against baselines that prove more difficult to adapt to the needs of different target audiences or require significant amounts of complex-simple parallel aligned data.

Target-Level Sentence Simplification as Controlled Paraphrasing

We report on novel investigations into training models that make sentences concise. We define the task and show that it is different from related tasks such as summarization and simplification. For evaluation, we release two test sets, consisting of 2000 sentences each, that were annotated by two and five human annotators, respectively. We demonstrate that conciseness is a difficult task for which zero-shot setups with large neural language models often do not perform well. Given the limitations of these approaches, we propose a synthetic data generation method based on round-trip translations. Using this data to either train Transformers from scratch or fine-tune T5 models yields our strongest baselines that can be further improved by fine-tuning on an artificial conciseness dataset that we derived from multi-annotator machine translation test sets.

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Next from EMNLP 2022

Text Simplification for Legal Domain: \{I\}nsights and Challenges

Similar lecture

ClauseRec: A Clause Recommendation Framework for AI-aided Contract Authoring