Learning target side syntactic structure has been shown to improve Neural Machine Translation (NMT). However, incorporating syntax through latent variables introduces additional complexity in inference, as the models need to marginalize over the latent syntactic structures. To avoid this, models often resort to greedy search which only allows them to explore a limited portion of the latent space. In this work, we introduce a new latent variable model, LaSyn, that captures the co-dependence between syntax and semantics, while allowing for effective and efficient inference over the latent space. LaSyn decouples direct dependence between successive latent variables, which allows its decoder to exhaustively search through the latent syntactic choices, while keeping decoding speed proportional to the size of the latent variable vocabulary. We implement LaSyn by modifying a transformer-based NMT system and design a neural expectation maximization algorithm that we regularize with part-of-speech information as the latent sequences. Evaluations on four different MT tasks show that incorporating target side syntax with LaSyn improves both translation quality, and also provides an opportunity to improve diversity.

Take 42% off by entering slhagiwara into the discount code box at checkout at manning.com. Natural language processing (NLP) is a set of tools and algorithms that help computers extract meaning from text. Turn to the next slide to find out more. 3. Apply NLP in your projects today Real-world Natural Language Processing teaches you how to create practical NLP applications without getting bogged down in complex language theory and the mathematics of deep learning. In it, you'll explore the core tools and techniques required to build a huge range of powerful NLP apps to help computers better understand humans. I saw a girl with a telescope… How's a computer to know which is right?

Exploration and analysis of potential data sources is a significant challenge in the application of NLP techniques to novel information domains. We describe HARE, a system for highlighting relevant information in document collections to support ranking and triage, which provides tools for post-processing and qualitative analysis for model development and tuning. We apply HARE to the use case of narrative descriptions of mobility information in clinical data, and demonstrate its utility in comparing candidate embedding features. We provide a web-based interface for annotation visualization and document ranking, with a modular backend to support interoperability with existing annotation tools. Our system is available online at https://github.com/OSU-slatelab/HARE.

Government agencies face similar challenges when it comes to understanding--and gaining intelligence from-- foreign language content. They need to process, manage and gain insight from large volumes of content locked away in different formats, often across multiple languages. And they need to do all of this as quickly as possible. It's no mean feat when you consider the mindboggling amounts of content being generated: 90% of the world's content was created over the past two years alone. Machine translation and text analytics have always been regarded as the two main ways for organizations and agencies to tackle this challenge.

This blog post is about the NAACL 2019 paper What makes a good conversation? How controllable attributes affect human judgments by Abigail See, Stephen Roller, Douwe Kiela and Jason Weston. On the left are tasks like Machine Translation (MT), which are less open-ended (i.e. Given the close correspondence between input and output, these tasks can be accomplished mostly (but not entirely) by decisions at the word/phrase level. On the right are tasks like Story Generation and Chitchat Dialogue, which are more open-ended (i.e. For these tasks, the ability to make high-level decisions (e.g.

Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation. However, these models are known to have several problems, especially in the context of chit-chat based dialogue systems: they tend to generate short and dull responses that are often too generic. Furthermore, these models do not ground conversational responses on knowledge and facts, resulting in turns that are not accurate, informative and engaging for the users. In this paper, we propose and experiment with a series of response generation models that aim to serve in the general scenario where in addition to the dialogue context, relevant unstructured external knowledge in the form of text is also assumed to be available for models to harness. Our proposed approach extends pointer-generator networks (See et al., 2017) by allowing the decoder to hierarchically attend and copy from external knowledge in addition to the dialogue context. We empirically show the effectiveness of the proposed model compared to several baselines including (Ghazvininejad et al., 2018; Zhang et al., 2018) through both automatic evaluation metrics and human evaluation on CONVAI2 dataset.

Build a text classification system (can be used for spam detection, sentiment analysis, and similar problems) Build a neural machine translation system (can also be used for chatbots and question answering) Build a sequence-to-sequence (seq2seq) model Build an attention model Build a memory network (for question answering based on stories) Understand what deep learning is for and how it is used Decent Python coding skills, especially tools for data science (Numpy, Matplotlib) Preferable to have experience with RNNs, LSTMs, and GRUs Preferable to have experience with Keras Preferable to understand word embeddings It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.

Language identification ("LI") is the problem of determining the natural language that a document or part thereof is written in. Automatic LI has been extensively researched for over fifty years. Today, LI is a key part of many text processing pipelines, as text processing techniques generally assume that the language of the input text is known. Research in this area has recently been especially active. This article provides a brief history of LI research, and an extensive survey of the features and methods used in the LI literature. We describe the features and methods using a unified notation, to make the relationships between methods clearer. We discuss evaluation methods, applications of LI, as well as off-the-shelf LI systems that do not require training by the end user. Finally, we identify open issues, survey the work to date on each issue, and propose future directions for research in LI.

Do you want a fun iPhone game that combines cats with a stealth lesson in artificial intelligence and machine learning? And thanks to the oddly titled while True: learn(), you're about to get your chance. Check out the game's new trailer, which landed ahead of this week's release of while True: learn() on iOS. As can be seen from the above trailer, the game's story deals with a cat who's also a master programmer. You set about making a cat-to-human translation program, and wind up developing a whole bunch of other AI tools, too.

There is a growing demand for applications which support speech, language identification, translation or transliteration from one language to another. Complex problems such as these can now be solved using advanced APIs that are readily available without having to reinvent the wheel – no machine learning expertise required! This blog starts off with a brief introduction to machine translation and then explores various topics like identifying the language and how to perform translation/transliteration of spoken or typed text using Microsoft's Translator Text API. In addition, we also discuss how translated or transliterated text can be integrated with LIUS. Machine Translation (MT) encompasses the various tasks involved in converting source text from one language to another.