Spain

ECAI 2020

SETNet : A Novel Semi-Supervised Approach for Semantic Parsing

semantic parsing

semi-supervised learning

multi-task learning

**Welcome to ACL-IJCNLP 2021!**

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP). 

As in previous years, the program of the conference includes a poster session, tutorials, workshops and demonstrations in addition to the main conference.


We were able to keep the registration fees similar to those charged for the virtual ACL 2020. The one fee allows attendance at the main conference and any/all tutorials and workshops. These fees would be $125 Regular Early and $175 Regular Late; $50 Student Early and $75 Student Late. Early registration closes at midnight July 11, 2021 (Eastern Daylight Time).

**Reminder:** It is ACL’s policy that at least one author of each accepted paper (including ACL Finding papers) must register for the conference.

**Reminder2:** Underline site will open closer to the event. If you already registered you will receive access detail

Registration is now open

ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

technical paper

ECAI2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI, including:




**An extensive programme of workshops, tutorials, and invited speakers**

**The Prestigious Applications of Intelligent Systems conference (PAIS)**

**The Frontiers in AI track sessions**

**The Lab To Market event**

**The Women in AI time**

**The EU Challenges forum**

… and many more!

ECAI 2020 featured a full programme of technical papers, as well as many AI-related events of interest for researchers, students and all attendants who are interested on contemporary AI.

A Knowledge-driven Approach to Explaining Black-box Models

Accounting for Observer’s Partial Observability in Stochastic Goal Recognition Design: Messing with the Marauder’s Map

Learning Conjunctive Information   of Signals in Multi-sensor Systems

Tree search for the Sequential Ordering Problem

Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition

Multi-Stage Transfer Learning with an Application to Selection Process

Improving Supervised Superpixel-Based Codebook Representations by Local Convolutional Features

Incorporating Semantic Dependencies Extracted from Knowledge Graphs into Joint Inference template-based Information Extraction

On Measuring Inconsistency in Relational Databases with Denial Constraints

Classifying the classifier: dissecting the weight space of neural networks

https://d3smihljt9218e.cloudfront.net/transcript/1181/subtitle/9779b51d087ec31bbe0184dcdc368e44.vtt

English (automatic)

SETNet : A Novel Semi-Supervised Approach for Semantic Parsing

Downloads

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A Knowledge-driven Approach to Explaining Black-box Models

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One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets

SETNet : A Novel Semi-Supervised Approach for Semantic Parsing

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Next from ECAI 2020

A Knowledge-driven Approach to Explaining Black-box Models

Similar lecture

One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets

Downloads