We present our method for tackling a legal case retrieval task by introducing our method of encoding documents by summarizing them into continuous vector space via our phrase scoring framework utilizing deep neural networks.
First, legal reasoning can be performed on the basis of the binary tree representation of the regulations.
The performance of legal text retrieval depends, to a large extent, on the representation of text, both query and legal documents.
In addition, to prove the generalization of our proposed model, we also conduct extensive experiments on three translation datasets IWLST14 German-English, IWSLT15 Vietnamese-English, WMT14 English-German, and show significant improvement.
Ranked #1 on Semantic Parsing on ATIS
In this paper, we introduce our approaches using Transformer-based models for different problems of the COLIEE 2021 automatic legal text processing competition.
COLIEE is an annual competition in automatic computerized legal text processing.
Ambiguity is a characteristic of natural language, which makes expression ideas flexible.
We show that a fully functional DeepLight system is able to robustly achieve high decoding accuracy (frame error rate < 0. 2) and moderately-high data goodput (>=0. 95Kbps) using a human-held smartphone camera, even over larger screen-camera distances (approx =2m).
This paper presents CovRelex, a scientific paper retrieval system targeting entities and relations via relation extraction on COVID-19 scientific papers.
Semantic parsing is a challenging task whose purpose is to convert a natural language utterance to machine-understandable information representation.
We present a retrieval-based model for the task by learning neural attentive text representation.
We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020.
On one hand, we adopt a summarization based model called encoded summarization which encodes a given document into continuous vector space which embeds the summary properties of the document.