Rethinking America's Illegal Drug Policy

This paper provides a critical review of the empirical and theoretical literatures on illegal drug policy, including cross-country comparisons, in order to evaluate three drug policy regimes: criminalization, legalization and “depenalization.” Drawing on the experiences of various states, as well as countries such as Portugal and the Netherlands, the paper attempts to identify cost-minimizing policies for marijuana and cocaine by assessing the differing ways in which the various drug regimes would likely change the magnitude and composition of the social costs of each drug. The paper updates and evaluates Jeffrey Miron’s 1999 national time series analysis of drug prohibition spending and the homicide rate, which underscores the lack of a solid empirical base for assessing the theoretically anticipated crime drop that would come from drug legalization. Nonetheless, the authors conclude that given the number of arrests for marijuana possession, and the costs of incarceration and crime systemic to cocaine criminalization, the current regime is unlikely to be cost-minimizing for either marijuana or cocaine.

SeamlessM4T-Massively Multilingual & Multimodal Machine Translation

What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-speech translation models have yet to achieve similar strides. More specifically, conventional speech-to-speech translation systems rely on cascaded systems that perform translation progressively, putting high-performing unified systems out of reach. To address these gaps, we introduce SeamlessM4T, a single model that supports speech-to-speech translation, speech-to-text translation, text-to-speech translation, text-to-text translation, and automatic speech recognition for up to 100 languages. To build this, we used 1 million hours of open speech audio data to learn self-supervised speech representations with w2v-BERT 2.0. Subsequently, we created a multimodal corpus of automatically aligned speech translations. Filtered and combined with human-labeled and pseudo-labeled data, we developed the first multilingual system capable of translating from and into English for both speech and text. On FLEURS, SeamlessM4T sets a new standard for translations into multiple target languages, achieving an improvement of 20% BLEU over the previous SOTA in direct speech-to-text translation. Compared to strong cascaded models, SeamlessM4T improves the quality of into-English translation by 1.3 BLEU points in speech-to-text and by 2.6 ASR-BLEU points in speech-to-speech. Tested for robustness, our system performs better against background noises and speaker variations in speech-to-text tasks compared to the current SOTA model. Critically, we evaluated SeamlessM4T on gender bias and added toxicity to assess translation safety. Finally, all contributions in this work are open-sourced and accessible at https://github.com/facebookresearch/seamless_communicatio

The interactions of rational, pragmatic agents lead to efficient language structure and use

Despite their diversity, languages around the world share a consistent set of properties and distributional regularities. For example, the distribution of word frequencies, the distribution of syntactic dependency lengths, and the presence of ambigu- ity are all remarkably consistent across languages. We dis- cuss a framework for studying how these system-level proper- ties emerge from local, in-the-moment interactions of rational, pragmatic speakers and listeners. To do so, we derive a novel objective function for measuring the communicative efficiency of linguistic systems in terms of the interactions of speakers and listeners. We examine the behavior of this objective in a series of simulations focusing on the communicative func- tion of ambiguity in language. These simulations suggest that rational pragmatic agents will produce communicatively effi- cient systems and that interactions between such agents pro- vide a framework for examining efficient properties of lan- guage structure and use more broadly

Determining the alternatives for scalar implicature

Successful communication regularly requires listeners to makepragmatic inferences — enrichments beyond the literal mean-ing of a speaker’s utterance. For example, when interpretinga sentence such as “Alice ate some of the cookies,” listenersroutinely infer that Alice did not eat all of them. A Griceanaccount of this phenomenon assumes the presence of alterna-tives (like “all of the cookies”) with varying degrees of infor-mativity, but it remains an open question precisely what thesealternatives are. To address this question, we collect empiricalmeasurements of speaker and listener judgments about vary-ing sets of alternatives across a range of scales and use these asinputs to a computational model of pragmatic inference. Thisapproach allows us to test hypotheses about how well differ-ent sets of alternatives predict pragmatic judgments by peo-ple. Our findings suggest that comprehenders likely considera broader set of alternatives beyond those logically entailed bythe initial message

Scalar alternatives

Determining the alternatives for scalar implicature literal and pragmatic listener studies

Deriving uniform information density behavior in pragmatic agents

The combinatorial expressivity of natural language enables speakers to communicate a single idea in myriad ways. Howdo speakers decide which utterance to use? Under the Uniform Information Density (UID) hypothesis, speakers shouldplan their utterances to minimize listener comprehension difficulty by spreading out new information, for example, byusing complementizers or avoiding contractions before high-surprisal content. We explore how UID behaviors may resultfrom pragmatic considerations (e.g., social reasoning in context) using a computational pragmatics model. We showthat artificial pragmatic agents communicating under noise conditions exhibit key UID effects: (A) speakers provide cuesbefore high surprisal content, (B) given a UID-cue, listeners infer oncoming content is high-surprisal, (C) synthetic corporagenerated from speakers reflects a signature UID effect: a positive relationship between likelihood of optional elementsand surprisal of oncoming content. Thus, UID may follow from more general principles of pragmatic communication inthe presence of noise

The interactions of rational, pragmatic agentslead to efficient language structure and use

Despite their diversity, languages around the world share aconsistent set of properties and distributional regularities. Forexample, the distribution of word frequencies, the distributionof syntactic dependency lengths, and the presence of ambigu-ity are all remarkably consistent across languages. We dis-cuss a framework for studying how these system-level proper-ties emerge from local, in-the-moment interactions of rational,pragmatic speakers and listeners. To do so, we derive a novelobjective function for measuring the communicative efficiencyof linguistic systems in terms of the interactions of speakersand listeners. We examine the behavior of this objective ina series of simulations focusing on the communicative func-tion of ambiguity in language. These simulations suggest thatrational pragmatic agents will produce communicatively effi-cient systems and that interactions between such agents pro-vide a framework for examining efficient properties of lan-guage structure and use more broadly

A Bayesian decision-making framework for replication

Replication is the cornerstone of science – but when and why? Not all studies need replication, especially when resources are limited. We propose that a decision-making framework based on Bayesian philosophy of science provides a basis for choosing which studies to replicate

A Bayesian decision-making framework for replication

Replication is the cornerstone of science – but when and why? Not all studies need replication, especially when resources are limited. We propose that a decision-making framework based on Bayesian philosophy of science provides a basis for choosing which studies to replicate