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opencog - A framework for integrated Artificial Intelligence & Artificial General Intelligence (AGI)

  •    Scheme

OpenCog is a framework for developing AI systems, especially appropriate for integrative multi-algorithm systems, and artificial general intelligence systems. Though much work remains to be done, it currently contains a functional core framework, and a number of cognitive agents at varying levels of completion, some already displaying interesting and useful functionalities alone and in combination. With the exception of MOSES and the CogServer, all of the above are in active development, are half-baked, poorly documented, mis-designed, subject to experimentation, and generally in need of love an attention. This is where experimentation and integration are taking place, and, like any laboratory, things are a bit fluid and chaotic.

multiffn-nli - Implementation of the multi feed-forward network architecture by Parikh et al

  •    Python

This code is a Tensorflow implementation of the models described in A Decomposable Attention Model for Natural Language Inference and Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference (for the latter, only the sequential model is implemented). The code was only tested in Python 2.7. The current version run on tensorflow 1.2.

jack - Jack the Reader

  •    Python

Jack the Reader - or jack, for short - is a framework for building and using models on a variety of tasks that require reading comprehension. For more informations about the overall architecture, we refer to Jack the Reader – A Machine Reading Framework (ACL 2018). To install Jack, install requirements and TensorFlow. In case you want to use PyTorch for writing models, please install PyTorch as well.

SPM_toolkit - Neural network toolkit for sentence pair modeling.

  •    Python

The SPM_toolkit contains 4 state-of-the-art neural networks for sentence pair modeling tasks, including: Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering.

ccg2lambda - Provide Semantic Parsing solutions and Natural Language Inferences for multiple languages following the idea of the syntax-semantics interface

  •    Python

This is a tool to derive formal semantic representations of natural language sentences given CCG derivation trees and semantic templates. (all tests should pass, except a few expected failures).

reasoning-about-entailment-tensorflow - :school: Tensorflow implementation of "Reasoning About Entailment with Neural Attention"

  •    Python

Tensorflow implementation of Reasoning about Entailment with Neural Attention, a paper which addresses the problem of Natural Language Inference with an end-to-end Neural Network architecture. The paper was a collaboration from Deepmind, Oxford University, and University College London. While most approaches to automatically recognizing entailment relations have used classifiers employing hand engineered features derived from complex natural language processing pipelines, in practice their performance has been only slightly better than bag-of-word pair classifiers using only lexical similarity. The only attempt so far to build an end-to-end differentiable neural network for entailment failed to outperform such a simple similarity classifier. In this paper, we propose a neural model that reads two sentences to determine entailment using long short-term memory units. We extend this model with a word-by-word neural attention mechanism that encourages reasoning over entailments of pairs of words and phrases. Furthermore, we present a qualitative analysis of attention weights produced by this model, demonstrating such reasoning capabilities. On a large entailment dataset this model outperforms the previous best neural model and a classifier with engineered features by a substantial margin. It is the first generic end-to-end differentiable system that achieves state-of-the-art accuracy on a textual entailment dataset.

BEGIN-dataset - A benchmark dataset for evaluating dialog system and natural language generation metrics


A benchmark dataset for evaluating dialog system and natural language generation metrics. See our paper "Evaluating Groundedness in Dialogue Systems: The BEGIN Benchmark" at https://arxiv.org/abs/2105.00071 . This is not an officially supported Google product. This means that Google may not regularly release updates or provide support in conjunction with your use of this dataset.

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