Neural approaches to conversational AI
This tutorial surveys neural approaches to conversational AI that were developed in the last
few years. We group conversational systems into three categories:(1) question answering …
few years. We group conversational systems into three categories:(1) question answering …
Statistical machine translation
A Lopez - ACM Computing Surveys (CSUR), 2008 - dl.acm.org
Statistical machine translation (SMT) treats the translation of natural language as a machine
learning problem. By examining many samples of human-produced translation, SMT …
learning problem. By examining many samples of human-produced translation, SMT …
Chatgpt perpetuates gender bias in machine translation and ignores non-gendered pronouns: Findings across bengali and five other low-resource languages
S Ghosh, A Caliskan - Proceedings of the 2023 AAAI/ACM Conference …, 2023 - dl.acm.org
In this multicultural age, language translation is one of the most performed tasks, and it is
becoming increasingly AI-moderated and automated. As a novel AI system, ChatGPT claims …
becoming increasingly AI-moderated and automated. As a novel AI system, ChatGPT claims …
Overview of the Transformer-based Models for NLP Tasks
In 2017, Vaswani et al. proposed a new neural network architecture named Transformer.
That modern architecture quickly revolutionized the natural language processing world …
That modern architecture quickly revolutionized the natural language processing world …
A neural network approach to context-sensitive generation of conversational responses
We present a novel response generation system that can be trained end to end on large
quantities of unstructured Twitter conversations. A neural network architecture is used to …
quantities of unstructured Twitter conversations. A neural network architecture is used to …
Lexically constrained decoding for sequence generation using grid beam search
We present Grid Beam Search (GBS), an algorithm which extends beam search to allow the
inclusion of pre-specified lexical constraints. The algorithm can be used with any model that …
inclusion of pre-specified lexical constraints. The algorithm can be used with any model that …
[PDF][PDF] Semantic parsing via paraphrasing
A central challenge in semantic parsing is handling the myriad ways in which knowledge
base predicates can be expressed. Traditionally, semantic parsers are trained primarily from …
base predicates can be expressed. Traditionally, semantic parsers are trained primarily from …
[PDF][PDF] Building a semantic parser overnight
How do we build a semantic parser in a new domain starting with zero training examples?
We introduce a new methodology for this setting: First, we use a simple grammar to generate …
We introduce a new methodology for this setting: First, we use a simple grammar to generate …
Learning to generate pseudo-code from source code using statistical machine translation
Pseudo-code written in natural language can aid the comprehension of source code in
unfamiliar programming languages. However, the great majority of source code has no …
unfamiliar programming languages. However, the great majority of source code has no …
Outside the closed world: On using machine learning for network intrusion detection
In network intrusion detection research, one popular strategy for finding attacks is monitoring
a network's activity for anomalies: deviations from profiles of normality previously learned …
a network's activity for anomalies: deviations from profiles of normality previously learned …