BERT got a Date: Introducing Transformers to Temporal Tagging

by   Satya Almasian, et al.

Temporal expressions in text play a significant role in language understanding and correctly identifying them is fundamental to various retrieval and natural language processing systems. Previous works have slowly shifted from rule-based to neural architectures, capable of tagging expressions with higher accuracy. However, neural models can not yet distinguish between different expression types at the same level as their rule-based counterparts. In this work, we aim to identify the most suitable transformer architecture for joint temporal tagging and type classification, as well as, investigating the effect of semi-supervised training on the performance of these systems. Based on our study of token classification variants and encoder-decoder architectures, we present a transformer encoder-decoder model using the RoBERTa language model as our best performing system. By supplementing training resources with weakly labeled data from rule-based systems, our model surpasses previous works in temporal tagging and type classification, especially on rare classes. Our code and pre-trained experiments are available at:



page 1

page 2

page 3

page 4


BNLP: Natural language processing toolkit for Bengali language

BNLP is an open source language processing toolkit for Bengali language ...

Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction

This paper investigates how to effectively incorporate a pre-trained mas...

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Pre-trained models for Natural Languages (NL) like BERT and GPT have bee...

A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch Novels and News

We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018...

Adversarial Alignment of Multilingual Models for Extracting Temporal Expressions from Text

Although temporal tagging is still dominated by rule-based systems, ther...

Auto-tagging of Short Conversational Sentences using Natural Language Processing Methods

In this study, we aim to find a method to auto-tag sentences specific to...

Holistic static and animated 3D scene generation from diverse text descriptions

We propose a framework for holistic static and animated 3D scene generat...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.