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Register a new userA Free Format Legal Question Answering System
تنسيق مجاني مسألة الإجابة على النظام
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نقدم نظام إجابة استدعاء الاسترجاع على المعلومات للإجابة على الأسئلة القانونية.لا يقتصر النظام على مجموعة محددة مسبقا من الأسئلة أو الأنماط ويستخدم كلا من البحث المتساقط والشركات التجريبية للمدخلات لنظام إعادة الإجابة القائمة على برت.يتم استخدام مجموعة من المجال العام والبيانات القانونية للتدريب.نظام الإجابة على السؤال الطبيعي في الإنتاج ويستخدم تجاريا.
We present an information retrieval-based question answer system to answer legal questions. The system is not limited to a predefined set of questions or patterns and uses both sparse vector search and embeddings for input to a BERT-based answer re-ranking system. A combination of general domain and legal domain data is used for training. This natural question answering system is in production and is used commercially.
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NLP research in Hebrew has largely focused on morphology and syntax, where rich annotated datasets in the spirit of Universal Dependencies are available. Semantic datasets, however, are in short supply, hindering crucial advances in the development o
f NLP technology in Hebrew. In this work, we present ParaShoot, the first question answering dataset in modern Hebrew. The dataset follows the format and crowdsourcing methodology of SQuAD, and contains approximately 3000 annotated examples, similar to other question-answering datasets in low-resource languages. We provide the first baseline results using recently-released BERT-style models for Hebrew, showing that there is significant room for improvement on this task.
The evaluation of question answering models compares ground-truth annotations with model predictions. However, as of today, this comparison is mostly lexical-based and therefore misses out on answers that have no lexical overlap but are still semanti
cally similar, thus treating correct answers as false. This underestimation of the true performance of models hinders user acceptance in applications and complicates a fair comparison of different models. Therefore, there is a need for an evaluation metric that is based on semantics instead of pure string similarity. In this short paper, we present SAS, a cross-encoder-based metric for the estimation of semantic answer similarity, and compare it to seven existing metrics. To this end, we create an English and a German three-way annotated evaluation dataset containing pairs of answers along with human judgment of their semantic similarity, which we release along with an implementation of the SAS metric and the experiments. We find that semantic similarity metrics based on recent transformer models correlate much better with human judgment than traditional lexical similarity metrics on our two newly created datasets and one dataset from related work.
This study describes the development of a Portuguese Community-Question Answering benchmark in the domain of Diabetes Mellitus using a Recognizing Question Entailment (RQE) approach. Given a premise question, RQE aims to retrieve semantically similar
, already answered, archived questions. We build a new Portuguese benchmark corpus with 785 pairs between premise questions and archived answered questions marked with relevance judgments by medical experts. Based on the benchmark corpus, we leveraged and evaluated several RQE approaches ranging from traditional information retrieval methods to novel large pre-trained language models and ensemble techniques using learn-to-rank approaches. Our experimental results show that a supervised transformer-based method trained with multiple languages and for multiple tasks (MUSE) outperforms the alternatives. Our results also show that ensembles of methods (stacking) as well as a traditional (light) information retrieval method (BM25) can produce competitive results. Finally, among the tested strategies, those that exploit only the question (not the answer), provide the best effectiveness-efficiency trade-off. Code is publicly available.
In this research we addressed the legal system of free zones in
Syria, where we showed the identity of these companies, their
advantages, and their work field. Then, we clarified the legal
provisions which govern the work of these companies in ter
ms
of their legal form, their nationalities, their qualities, their
establishment, and their end. Among the most important
results of the research was that the nature of companies of free
zones was different from the nature of national companies,
where this requires formulating special legal rules that organize
the work of these companies. In fact, the Syrian legislator was
not successful in this regard in the light of the lack of legal
provisions which regulate the work of such companies, where
this may raise confusion between the provisions of these
companies and the other provisions related to the work of
companies in general outside the scope of free zones. The
research concluded with many recommendations, the most
important of which was formulating special legal regulation for
the companies of free zones in terms of their establishment,
nationalities, work, and distinction from other companies in
terms of legal regulation.
A question answering system that in addition to providing an answer provides an explanation of the reasoning that leads to that answer has potential advantages in terms of debuggability, extensibility, and trust. To this end, we propose QED, a lingui
stically informed, extensible framework for explanations in question answering. A QED explanation specifies the relationship between a question and answer according to formal semantic notions such as referential equality, sentencehood, and entailment. We describe and publicly release an expert-annotated dataset of QED explanations built upon a subset of the Google Natural Questions dataset, and report baseline models on two tasks---post- hoc explanation generation given an answer, and joint question answering and explanation generation. In the joint setting, a promising result suggests that training on a relatively small amount of QED data can improve question answering. In addition to describing the formal, language-theoretic motivations for the QED approach, we describe a large user study showing that the presence of QED explanations significantly improves the ability of untrained raters to spot errors made by a strong neural QA baseline.
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