In this provocative book, Barwise and Perry tackle the slippery subject of \"meaning, \" a subject that has long vexed linguists, language philosophers, and logicians.

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Situations and Attitudes.

The Cambridge Grammar of the English Language

The past 3 years of work in NLP have been characterized by the development and deployment of ever larger language models, especially for English. BERT, its variants, GPT-2/3, and others, most recently Switch-C, have pushed the boundaries of the possible both through architectural innovations and through sheer size. Using these pretrained models and the methodology of fine-tuning them for specific tasks, researchers have extended the state of the art on a wide array of tasks as measured by leaderboards on specific benchmarks for English. In this paper, we take a step back and ask: How big is too big? What are the possible risks associated with this technology and what paths are available for mitigating those risks? We provide recommendations including weighing the environmental and financial costs first, investing resources into curating and carefully documenting datasets rather than ingesting everything on the web, carrying out pre-development exercises evaluating how the planned approach fits into research and development goals and supports stakeholder values, and encouraging research directions beyond ever larger language models.

https://dl.acm.org/doi/pdf/10.1145/3442188.3445922

On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? &#x1f99c;

自然界和人类社会中存在着大量的中间状态，人类的语言也是如此。大多数人在出生后首先习得和掌握的是他的母语（mother tongue），在以后的人生中，会有机会学习第二语言（secongd language）或目的语（target language）。所谓中介语是指第二语言学习者建构起来的介于母语和目的语之间的过渡性语言，它处于不断的发展变化过程中，并逐渐向目的语靠近。

中介语（interlanguage）初探

Understanding second language acquisition

Much research in translation studies indicates that translated texts are ontologically different from original non-translated ones. Translated texts, in any language, can be considered a dialect of that language, known as ‘translationese’. Several characteristics of translationese have been proposed as universal in a series of hypotheses. In this work, we test these hypotheses using a computational methodology that is based on supervised machine learning. We define several classifiers that implement various linguistically informed features, and assess the degree to which different sets of features can distinguish between translated and original texts. We demonstrate that some feature sets are indeed good indicators of translationese, thereby corroborating some hypotheses, whereas others perform much worse (sometimes at chance level), indicating that some ‘universal’ assumptions have to be reconsidered.

In memoriam: Miriam Shlesinger, 1947–2012

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On the features of translationese

We investigate the differences between language models compiled from original target-language texts and those compiled from texts manually translated to the target language. Corroborating established observations of Translation Studies, we demonstrate that the latter are significantly better predictors of translated sentences than the former, and hence fit the reference set better. Furthermore, translated texts yield better language models for statistical machine translation than original texts.

Language Models for Machine Translation: Original vs. Translated Texts

Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and “fake news”. Here, we draw on two concepts from political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered). We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia. We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.

/pdf/framing-and-agenda-setting-in-russian-news-a-computational-jrjaj2i7i6.pdf

Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

We describe a suite of standards, resources and tools for computational encoding and processing of Modern Hebrew texts. These include an array of XML schemas for representing linguistic resources; a variety of text corpora, raw, automatically processed and manually annotated; lexical databases, including a broad-coverage monolingual lexicon, a bilingual dictionary and a WordNet; and morphological processors which can analyze, generate and disambiguate Hebrew word forms. The resources are developed under centralized supervision, so that they are compatible with each other. They are freely available and many of them have already been used for several applications, both academic and industrial.

/pdf/language-resources-for-hebrew-35rp72qd8b.pdf

Language resources for Hebrew

The language that we produce reflects our personality, and various personal and demographic characteristics can be detected in natural language texts. We focus on one particular personal trait of the author, gender, and study how it is manifested in original texts and in translations. We show that author’s gender has a powerful, clear signal in originals texts, but this signal is obfuscated in human and machine translation. We then propose simple domain-adaptation techniques that help retain the original gender traits in the translation, without harming the quality of the translation, thereby creating more personalized machine translation systems.

/pdf/personalized-machine-translation-preserving-original-author-4pi5necwyu.pdf

Shuly Wintner

Papers

On the features of translationese

Language Models for Machine Translation: Original vs. Translated Texts

Framing and Agenda-setting in Russian News: a Computational Analysis of Intricate Political Strategies

Language resources for Hebrew

Personalized Machine Translation: Preserving Original Author Traits