Shikhar Bharadwaj, Chin-Jou Li, Yoonjae Kim, Kwanghee Choi, Eunjung Yeo, Ryan Soh-Eun Shim, Hanyu Zhou, Brendon Boldt, Karen Rosero, Kalvin Chang, Darsh Agrawal, Keer Xu, Chao-Han Huck Yang, Jian Zhu, Shinji Watanabe, David R. Mortensen. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

We build rule-based emergent language (EL) agents using form-meaning mappings induced from ELs ("morphological phrasebooks") and test their communicative performance in the EL environment with its neural network agents.This contributes three things: First, it assesses the quality of the morphemes discovered by the induction algorithm in situ, which we find to be effective for communicating in the EL.Second, it allows us to uncover morphosyntactic properties of EL through ablating the algorithms which induce and utilize morphemes, showing that the ELs rely on repetition as well as morpheme ordering to convey meaning.Third, we find that the normalized pointwise mutual information of forms and meanings in the morphemes serves as a metric of compositionality that is more closely correlated with the ability of the phrasebook-agents to "speak" and "hear" an EL than existing metrics such as topographic similarity.

In this paper, we design a signalling gamebased emergent communication environment to generate state-of-the-art emergent languages in terms of similarity to human language.This is done with hyperparameter optimization, using XferBench as the objective function.Xfer-Bench quantifies the statistical similarity of emergent language to human language by measuring its suitability for deep transfer learning to human language.Additionally, we demonstrate the predictive power of entropy on the transfer learning performance of emergent language as well as corroborate previous results on the entropy-minimization properties of emergent communication systems.Finally, we report generalizations regarding what hyperparameters produce more realistic emergent languages, that is, ones which transfer better to human language.

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 on mutual information between forms and meanings, (2) selects the highest-weighted pair, (3) removes it from the corpus, and (4) repeats the process to induce further morphemes (i.e., Count, Select, Ablate, Repeat).The effectiveness of CSAR is first validated on procedurally generated datasets and compared against baselines for related tasks.Second, we validate CSAR's performance on human language data to show that the algorithm makes reasonable predictions in adjacent domains.Finally, we analyze a handful of emergent languages, quantifying linguistic characteristics like degree of synonymy and polysemy.

Brendon Boldt, David Mortensen. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2024.

Shrimai Prabhumoye, Brendon Boldt, Ruslan Salakhutdinov, Alan W Black. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

An implicit association test is a human psychological test used to measure subconscious associations. While widely recognized by psychologists as an effective tool in measuring attitudes and biases, the validity of the results can be compromised if a subject does not follow the instructions or attempts to manipulate the outcome. Compared to previous work, we collect training data using a more generalized methodology. We train a variety of different classifiers to identify a participant's first attempt versus a second possibly compromised attempt. To compromise the second attempt, participants are shown their score and are instructed to change it using one of five randomly selected deception methods. Compared to previous work, our methodology demonstrates a more robust and practical framework for accurately identifying a wide variety of deception techniques applicable to the IAT.