The application of Differential Privacy to Natural Language Processing techniques has emerged in relevance in recent years, with an increasing number of studies published in established NLP outlets. In particular, the adaptation of Differential Privacy for use in NLP tasks has first focused on the $\textit{word-level}$, where calibrated noise is added to word embedding vectors to achieve "noisy" representations. To this end, several implementations have appeared in the literature, each presenting an alternative method of achieving word-level Differential Privacy. Although each of these includes its own evaluation, no comparative analysis has been performed to investigate the performance of such methods relative to each other. In this work, we conduct such an analysis, comparing seven different algorithms on two NLP tasks with varying hyperparameters, including the $\textit{epsilon ($\varepsilon$)}$ parameter, or privacy budget. In addition, we provide an in-depth analysis of the results with a focus on the privacy-utility trade-off, as well as open-source our implementation code for further reproduction. As a result of our analysis, we give insight into the benefits and challenges of word-level Differential Privacy, and accordingly, we suggest concrete steps forward for the research field.

This paper presents a novel approach to assist students with dyslexia, ADHD, and short attention span in digesting any text-based information more efficiently. The proposed solution utilizes the Multilayer Perceptron (MLP) algorithm for complex text processing and summarization tasks. The tool leverages the T5 (Text-to-Text Transfer Transformer) model from Hugging Face, which treats every NLP task as a text generation task. The model is fine-tuned on specific tasks using a smaller dataset. The NLTK's Punkt Sentence Tokenizer is used to divide a text into a list of sentences. The application is served using Flask, a lightweight web server and framework. The tool also applies principles from Bionic Reading to enhance readability, which includes a bolding function and adjustments to line, word, and character spacing. The paper discusses the methodology, implementation, and results of the AI-based speed reading tool.

Relation extraction typically aims to extract semantic relationships between entities from the unstructured text. One of the most essential data sources for relation extraction is the spoken language, such as interviews and dialogues. However, the error propagation introduced in automatic speech recognition (ASR) has been ignored in relation extraction, and the end-to-end speech-based relation extraction method has been rarely explored. In this paper, we propose a new listening information extraction task, i.e., speech relation extraction. We construct the training dataset for speech relation extraction via text-to-speech systems, and we construct the testing dataset via crowd-sourcing with native English speakers. We explore speech relation extraction via two approaches: the pipeline approach conducting text-based extraction with a pretrained ASR module, and the end2end approach via a new proposed encoder-decoder model, or what we called SpeechRE. We conduct comprehensive experiments to distinguish the challenges in speech relation extraction, which may shed light on future explorations. We share the code and data on https://github.com/wutong8023/SpeechRE.

In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results in three research domains: (i) Building clean, web-mined datasets for 1500+ languages by leveraging semi-supervised pre-training for language identification and developing data-driven filtering techniques; (ii) Developing practical MT models for under-served languages by leveraging massively multilingual models trained with supervised parallel data for over 100 high-resource languages and monolingual datasets for an additional 1000+ languages; and (iii) Studying the limitations of evaluation metrics for these languages and conducting qualitative analysis of the outputs from our MT models, highlighting several frequent error modes of these types of models. We hope that our work provides useful insights to practitioners working towards building MT systems for currently understudied languages, and highlights research directions that can complement the weaknesses of massively multilingual models in data-sparse settings.

Summarization of long sequences into a concise statement is a core problem in natural language processing, requiring non-trivial understanding of the input. Based on the promising results of graph neural networks on highly structured data, we develop a framework to extend existing sequence encoders with a graph component that can reason about long-distance relationships in weakly structured data such as text. In an extensive evaluation, we show that the resulting hybrid sequence-graph models outperform both pure sequence models as well as pure graph models on a range of summarization tasks.

The drones fell out of the sky over China Lake, California, like a colony of bats fleeing a cave in the night. Over 100 of them dropped from the bellies of three Boeing F/A-18 Super Hornet fighter jets, their sharp angles cutting across the clear blue sky. As they encircled their target, the mechanical whir of their flight sounded like screaming. This was the world's largest micro-drone swarm test. Conducted in October 2016 by the Department of Defense's Strategic Capabilities Office and the Navy's Air Systems Command, the test was the latest step in what could be termed a swarm-drone arms race.

ISLAMABAD – A Taliban official says a suspected U.S. drone strike the previous day killed a top commander of the militant Haqqani network -- the man who in 2014 accompanied U.S. Army Sgt. Bowe Bergdahl when he was handed over to U.S. authorities. The Taliban official identified the man as Qari Abdullah, saying he died in the "area of Khost." Pakistani intelligence officials had earlier said a suspected U.S. strike hit in Pakistan's lawless tribal region bordering Afghanistan's Khost, a Haqqani stronghold, killing two militants. The Taliban official wouldn't confirm it was the same strike.