# HLTCHKUST/Mem2Seq

<https://repo.masters-masters.com/en/repos/HLTCHKUST/Mem2Seq>

[Home](https://repo.masters-masters.com/en) › [HLTCHKUST](https://repo.masters-masters.com/en/owners/HLTCHKUST) › [Mem2Seq](https://repo.masters-masters.com/en/repos/HLTCHKUST/Mem2Seq)

HLTCHKUST/Mem2Seq has 351 stars on GitHub. Its RepoMasters card: Alpha tier. Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented…

| Field | Value |
| --- | --- |
| Owner | [HLTCHKUST](https://repo.masters-masters.com/en/owners/HLTCHKUST) |
| Stars | 351 |
| Forks | 104 |
| Language | [Python](https://repo.masters-masters.com/en/languages/python) |
| License | MIT |
| Created | 2018 |
| Topics | memory-network, deep-learning, dialogue-systems, end-to-end-learning |
| Tier | [Alpha (250 to 699 ★)](https://repo.masters-masters.com/en/ranks/b_movie) |
| View on GitHub | [HLTCHKUST/Mem2Seq](https://github.com/HLTCHKUST/Mem2Seq) |

## Repository

Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems

The tier for repos with 250 to 699 ★. Published odds per card pulled: 20%. Rarity: Uncommon.

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Data: GitHub · Illustration drawn by RepoMasters from the repo’s ID and its language color.
