5 papers
CS-YODAS: A Mined Dataset of In-the-Wild Code-Switched Speech
Brian Yan, Qingzheng Wang, Matthew Wiesner +9
We present CS-YODAS, a Creative Commons-licensed dataset of in-the-wild code-switched speech mined from multilingual YouTube data. Code-switching (CS), or the alternation between l…
Scaling Self-Supervised Speech Models Uncovers Deep Linguistic Relationships: Evidence from the Pacific Cluster
Minu Kim, Hoirin Kim, David R. Mortensen
Similarities between language representations derived from Self-Supervised Speech Models (S3Ms) have been observed to primarily reflect geographic proximity or surface typological…
ReaComp: Compiling LLM Reasoning into Symbolic Solvers for Efficient Program Synthesis
Atharva Naik, Yash Mathur, Prakam +2
LLMs can solve program synthesis tasks but remain inefficient and unreliable on hard instances requiring large combinatorial search. Given a small set of reasoning traces, we use c…
Searching for the Most Human-like Emergent Language
Brendon Boldt, David Mortensen
In this paper, we design a signalling game-based emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language. This i…
Morpheme Induction for Emergent Language
Brendon Boldt, David Mortensen
We introduce CSAR, an algorithm for inducing morphemes from emergent language corpora of parallel utterances and meanings. It is a greedy algorithm that (1) weights morphemes based…