Factors Influencing the Emergence of Dependency Length Minimization in Neural Agent Simulations

1 INTRODUCTION

• Earlier neural simulations reported both locality biases and absent short-dependency preferences, but differences in architectures, pretraining, and language design complicate comparisons.Some studies also used linearized meaning representations that could bias word-order outcomes.Source: §1 · p. 2
• The study adapts NeLLCom to paired speaking and listening agents learning artificial languages and communicating without pretraining or meaning representations biased toward a linear order.The miniature languages draw on a human artificial-language learning experiment, and dependency relations are modeled through word distributions and verb selectional preferences.Source: §1 · p. 2
• The experiments test whether listening noise, limited speaker capacity, and listener-side incremental interpretation affect DLM, alongside a partial comparison of RNN and GRU agents.The design distinguishes a default speaker production preference from a preference that becomes advantageous under incremental comprehension pressure.Source: §1 · p. 2
• In the full meaning space, word-order regularization overrides DLM; in the half meaning space, short-before-long ordering overrides DLM, while incremental listener processing elicits DLM.The incremental-processing result holds for both verb-initial and verb-final languages.Source: §1 · p. 3
• The results indicate that processing constraints can shape DLM preferences, motivating more realistic and cognitively plausible neural-agent simulations.The observed ordering reflects pressures beyond DLM alone.Source: §1 · pp. 2–3

2.1 Dependency Length Minimization

• DLM is operationalized as minimizing linear distance between grammatical heads and dependents, with shorter dependencies associated with lower search and working-memory demands.These processing costs motivate the proposed comprehension and production benefits of DLM.Source: §2.1 · p. 3
• Corpus analyses and artificial-language experiments report shorter dependencies or restructuring toward shorter dependencies, supporting DLM across languages and learners.The cited experiments found restructuring toward shorter dependencies across participant groups regardless of native-language order preferences.Source: §2.1 · p. 3
• DLM's surface ordering varies with head position, and word order can reflect competing pressures such as information locality, predictability, animacy, and lexical grouping.The cited work also reports weaker DLM tendencies in some head-final or morphologically rich languages.Source: §2.1 · p. 4

2.2 Neural Network-based Simulations

• Emergent communication models can test linguistic theories, but generic communication pressure may not yield efficient codes without specific constraints such as noise or incremental processing.The present framework is motivated by methodological gaps including linearized meanings, pretrained models, and limited attention to dynamic communication.Source: §2.2 · pp. 4–5
• Prior neural simulations found locality effects in some settings but mixed DLM-related preferences across artificial-language production and pretrained models.Linearized meanings may bias input reordering, while pretrained Transformers may reflect English training biases and lack human-like memory limits.Source: §2.2 · p. 5

3.1 Adaptation of the NeLLCom Framework

• Neural agents learn miniature-language mappings through supervised training, then communicate via reinforcement learning that rewards listeners’ recovery of intended meanings on unseen meanings.Speakers generate utterances from meanings, while listeners reconstruct meanings from utterances.Source: §3.1 · p. 5

3.2 Miniature Languages

• Languages mixed verb-initial VSO/VOS or verb-final SOV/OSV orders; fixed nine-token utterances varied only the linear distances from the verb to dependent heads.Each meaning had two possible orders and contained one long constituent; the design controlled sentence length while manipulating dependency length.Source: §3.2 · pp. 7–8

3.3 Conditional Word Distributions

• The study compared uniform input with Zipf-skewed distributions (s = 1.5), incorporating verb-dependent selectional preferences and a 32-word vocabulary.Animate nouns varied in their probabilities of occurring with verbs and taking agent or patient roles; other meaning items were uniform.Source: §3.3 · pp. 8–9

3.4 Noisy Communication

• Listening noise was modeled as independent token dropout, with each token masked at probability p ∈ {0, 0.1}.The 10% condition was selected to add communicative pressure without preventing reinforcement-learning convergence.Source: §3.4 · p. 9

3.5 Limited Speaker Capacity

• Speaker capacity was constrained by reducing hidden size from H = 512 to H = 256; H = 128 was excluded because it impaired accuracy and production stability.The reduced condition was chosen to retain successful communication while producing behavior distinct from the larger model.Source: §3.5 · p. 10

3.6 Incremental Processing

• Incremental processing trained an impatient listener to predict meaning from utterance prefixes, with rewards averaged across positions so earlier correct predictions earned greater reward.The method adapted the Impatient Listener architecture to make intermediate predictions during communication.Source: §3.6 · pp. 10–11

3.7 Evaluation Metrics

• Evaluation measured supervised speaker exact-match accuracy, listener whole-meaning and item-level accuracy, and reinforcement-learning meaning-reconstruction accuracy on unseen meanings.Plotted accuracies were averaged over random seeds unless otherwise stated.Source: §3.7 · pp. 11–12
• The study measures local-dependency production as a proportion, with 50% representing equal short- and long-dependency choices, and tests preferences and condition differences using Bayesian multilevel models.The proportion excludes productions not classifiable as local or long; models test whether the mean exceeds 50% and compare conditions.Source: §3.7 · p. 12

3.8 Training Details

• Six training-language types vary dependency distributions, with skewed mixed languages serving as a neutral baseline for detecting preferences developed during communication.Short, mixed, and long languages contain 100%, 50%, and 0% local-dependency utterances, respectively. Agents trained on 66.7% of meaning-utterance pairs for 60 supervised-learning epochs, then completed 60 communication epochs; production length was capped at 9. Experiment 1 used 20 random seeds per condition and Experiments 2–3 used 40.Source: §3.8 · p. 12

3.9 Meaning Space

• Experiment 1 uses a full meaning space balancing subject- and object-modified scenes, whereas Experiments 2 and 3 simplify meanings to one modified entity.Experiment 1 meanings contain nine items and about 5,000 meaning-utterance pairs per language; the follow-up meaning spaces contain six items and 2,543 pairs per language.Source: §3.9 · pp. 12–13

3.10 Summary of Experiments

• Across experiments, word-order regularization overrides DLM in the full meaning space, short-before-long ordering overrides DLM in the half meaning space, and incremental processing elicits DLM in both verb-initial and verb-final languages.The overview attributes the incremental-processing effect to the Impatient Listener architecture.Source: §3.10 · p. 13

4 EXPERIMENT 1: WORD ORDER REGULARIZATION OVERRIDES DLM

• Experiment 1 used the full meaning space and added both subject and object markers to rule out a general preference for producing marked constituents first.Training setup details are summarized in Table 2.Source: §4 · p. 13, Table L · p. 14

4.1 Communication Success and Production Preferences

• Communication accuracy increased across RL epochs in both conditions, with higher accuracy in baseline; noise produced no systematic production deviation.Production trends under noise were broadly similar to those without noise, so the no-noise verb-initial skewed mixed language was used for the main analysis.Source: §4.1 · pp. 13–14

4.1.1 Supervised Learning Phase

• Agents preserved the training data’s balanced distribution after supervised learning rather than shifting toward short-dependency utterances.This pattern was consistent with probability matching after supervised learning.Source: §4.1.1 · pp. 14–15

4.1.2 Communication Phase

• After communication, agents regularized to either VSO or VOS while short- and long-dependency productions stayed balanced, showing no DLM preference.The seed-level patterns show that individual agents could regularize even while aggregate local-dependency proportions remained near the 50% input baseline.Source: §4.1.2 · p. 15, §4.1.1 · p. 15

4.2 Discussion

• Because different scenes required different orders for shorter dependencies, agents favored fixed-order regularization, which could override efficiency gains even with markers available.Adding both markers or changing model parameters did not alter the overall pattern.Source: §4.2 · pp. 15–16
• This pattern aligns with prior emergent-communication findings on reduced word-order entropy, unlike human experiments where DLM could persist against regularization.The authors suggest that neural agents’ preference for consistency may reduce variability and override potential DLM gains under communicative pressure.Source: §4.2 · pp. 15–16
• Similar regularization appeared with uniform languages, dropout, and varying speaker capacity, but communication did not train successfully with the Impatient Listener in the full meaning space.This limits the reported comparison involving that architecture to settings other than the full meaning space.Source: §4.2 · p. 15

5.1 Experimental setup

• Experiment 2 used half the meaning space and tested listening noise and reduced speaker capacity, separately and together, alongside baseline communication.The half-space setup allowed order and dependency-length regularization to occur concurrently; the reduced-capacity condition used smaller speaker hidden layers.Source: §5 · p. 16, §5.1 · p. 16

5.2 Learning Accuracy

• Listening accuracy fell under noise, while skewed word distributions improved listening robustness; shorter dependencies did not provide a listening learnability advantage.The authors report a small listening advantage for long-dependency languages, unlike an earlier study.Source: §5.2 · p. 17

5.3 Communication Success

• Communication accuracy exceeded 95% at convergence, but noise reduced success and mixed-dependency languages performed slightly worse than consistent-dependency languages.Skewed distributions were more robust to noise, and short-dependency languages had a slight communication advantage, especially under skew.Source: §5.3 · p. 18

5.4 Production Preferences for Verb-initial Languages

• After supervised learning, verb-initial agents remained near the 50% baseline; baseline communication later shifted production toward short dependencies.After 60 communication epochs, the mean local-dependency proportion reached b = 89.07, and 38 of 40 seeds regularized toward shorter dependencies.Source: §5.4 · pp. 18–19
• For verb-initial agents, noise and reduced speaker capacity strengthened short-dependency production, with their combination producing the strongest bias; GRUs were more stable than RNNs.All seeds favored shorter dependencies under reduced capacity and under noise; GRU agents showed fewer production differences between baseline and noise conditions.Source: §5.4 · pp. 19–20

5.5 Production Preferences for Verb-final Languages

• Across verb-initial and verb-final languages, agents favored short-before-long ordering, which matched DLM only in verb-initial languages.In verb-final languages, long-dependency utterances could reflect short-before-long ordering; both RNN and GRU agents showed this pattern.Source: §5.4 · p. 19, §5.5 · p. 20, §5.6 · p. 20
• In verb-final languages, agents increasingly produced long-dependency utterances, which corresponded to short-before-long ordering rather than DLM.Agents consistently produced OSV order, and the pattern appeared in both RNN- and GRU-based agents.Source: §5.5 · p. 20, Table 5 · p. 20

5.6 Discussion

• The authors interpret the cross-language pattern as a general short-before-long bias, not necessarily an explicit pressure to minimize dependency length.A production-side sequencing account is tentative because the study did not directly examine speaker hidden-state dynamics.Source: §5.6 · pp. 20–21

6.1 Experimental Setup

• Experiment 3 used vanilla RNN agents and compared Baseline, Impatient, and Impatient + Noise conditions; the reduced-capacity condition was excluded.The speaker model was already at the minimum size capable of reasonable communication accuracy.Source: §6 · p. 21, §6.1 · p. 21, §6.2 · p. 21

6.2 Communication Success

• With impatient listeners, communication accuracy initially dropped but recovered toward its initial level over several communication rounds.The authors considered this recovery sufficient to proceed with production-preference analysis.Source: §6.2 · pp. 21–22, Figure 8 · p. 22

6.3 Production Preferences

• Impatient Listeners increased short-dependency production in both verb-initial and verb-final languages; added noise produced no systematic further effect at later stages.The authors attribute the pattern to incremental comprehension pressure, and report a contrast with Experiment 2’s verb-final long-dependency preference.Source: §6.3 · p. 22, Figure 9 · p. 23, §6.4 · p. 23

6 EXPERIMENT 3: INCREMENTAL PROCESSING ELICITS DLM

• The Impatient Listener incrementally updates predictions and rewards productions yielding greater incremental accuracy, making partial interpretations more reliable.The authors connect this pressure to reduced temporary ambiguity in verb-final languages when action, agent, and patient are grouped together.Source: §6 · p. 21, §6.3 · p. 22

6.4 Discussion

• The authors conclude that incremental processing elicited more human-like production patterns, but verb-final agents had higher communication accuracy under the tested settings.They note that modest learning-parameter adjustments could improve verb-initial performance, including convergence to fully local-dependency production under some settings.Source: §6.3 · p. 22, §6.4 · p. 23

7 GENERAL DISCUSSION

• Incremental listener processing elicited DLM-compatible production; without it, word-order regularization or short-before-long ordering dominated.Shorter dependencies became communicatively advantageous when listeners were rewarded for interpreting utterances incrementally.Source: §7 · pp. 23–24

7.1 From Short-before-Long Bias to DLM in Neural Agents

• Without an incremental prediction listener, agents favored short-before-long ordering in both verb-initial and verb-final languages.The authors relate this pattern to form-driven production processes, while treating a recurrent-generation explanation as tentative.Source: §7.1 · p. 24
• Incremental listeners commit to partial interpretations online, and shorter dependencies may reduce temporary ambiguity for them.The authors suggest that listener expectations and processing strategies can shape production, while noting that encoding and generation may rely on different mechanisms.Source: §7.1 · p. 24
• Transformer-based agents were not successfully adapted, so predictions about their production preferences remain tentative.A direct comparison among RNN-, GRU-, and Transformer-based agents remains future work.Source: §7.1 · p. 24

7.2 Implications on Using Neural Networks to Simulate Language Emergence

• Neural simulations may reproduce human-observed patterns through mechanisms unlike human cognition, so emergent behavior needs validation against hypothesized cognitive mechanisms.The authors also note that reintroducing inductive biases may help neural models exhibit linguistically plausible behavior.Source: §7.2 · p. 25

7.3 Limitations and Future Extensions

• Processing constraints are modeled through an incremental listener and speaker-capacity manipulation, while more direct memory models and richer meanings remain future work.Current meanings are limited to simple transitive events and lack hierarchical recursion and discourse pragmatics; utterance length is fixed.Source: §7.3 · p. 25
• Learning and communication are separate phases, and training duration, architecture, initialization, and interaction structure may affect results.The authors propose testing more dynamic learning-communication and broader model settings; character-level dependency distance is another proposed direction.Source: §7.3 · pp. 25–26

8 CONCLUSIONS

• Only incremental processing yielded DLM-consistent productions in both verb-initial and verb-final languages; noise and limited speaker capacity instead favored short-before-long constituents.The short-before-long pattern is linked to production studies and often attributed to lexical accessibility or production efficiency.Source: §8 · p. 26
• The study incorporates listening noise, limited speaker capacity, and incremental sentence processing to make neural-agent simulations more cognitively plausible.Source: §8 · p. 26
• Perception, processing, and memory constraints can exert selective pressures whose interactions produce trade-offs or unexpected synergies.The conclusion presents computational modeling as a way to disentangle these contributions.Source: §8 · p. 26

A DATA GENERATION DETAILS

• Skewed language versions used ten shuffled animate nouns with probabilities from 0.501 to 0.016 for agent and patient roles given each verb.Uniform versions assigned equal probability to all animate nouns for each verb.Source: A DATA GENERATION DETAILS · p. 30
• Meaning generation combined adpositions, adjectives, and inanimate nouns, then sampled agents and patients from their verb-conditioned distributions.The procedure generated 2580 meanings before filtering out repeated meanings and cases where agent and patient were the same.Source: A DATA GENERATION DETAILS · p. 30
• The skewed-data procedure was designed to keep non-agent and non-patient meaning tokens equally probable while preserving the assigned conditional distributions.The method assumes equal probability for all meaning tokens other than agent and patient.Source: A DATA GENERATION DETAILS · p. 30
• For the uniform version, the authors generated all combinations of verbs, subjects, objects, and modifiers, then sampled an equal number of meanings.The resulting data distributions are shown in Figure A1.Source: A DATA GENERATION DETAILS · p. 30

B INDIVIDUAL PRODUCTION PATTERNS UNDER THE NOISY CONDITION IN EXPERIMENT 1

• Among 20 agents in the verb-initial skewed mixed-language setting with word dropout 0.1, about half regularized toward SO and half toward OS order.These patterns followed learning or communication.Source: B INDIVIDUAL PRODUCTION PATTERNS UNDER… · p. 30
• Production patterns for other settings, including verb-final and uniform languages with dropout, were described as similar in regularization strategy and were not shown.Source: B INDIVIDUAL PRODUCTION PATTERNS UNDER… · p. 30
• A separate appendix entry covers 40 speaking agents during communication learning with only the SM meaning space in a verb-initial skewed mixed language.Source: B INDIVIDUAL PRODUCTION PATTERNS UNDER… · p. 30

• Listening accuracy is computed separately for each individual meaning item in the verb-initial language.Source: C PER-CATEGORY LISTENING ACCURACY IN… · p. 30
• The appendix also presents average listening accuracy for the verb-initial language.Source: C PER-CATEGORY LISTENING ACCURACY IN… · p. 30
• Both accuracy views are shown as a function of training epoch.Source: C PER-CATEGORY LISTENING ACCURACY IN… · p. 30

• Learning and communication accuracies were mostly similar for verb-initial and verb-final languages.Source: D LEARNING AND COMMUNICATION… · p. 30
• Figure A1 plots the conditional probabilities of Agent and Patient given Actions.Source: D LEARNING AND COMMUNICATION… · p. 31
• Figure A1 contrasts skewed languages in the top panel with uniform languages in the bottom panel.Source: D LEARNING AND COMMUNICATION… · p. 31

• In baseline, verb-initial agents reduced short-dependency VOS errors while long-dependency VSO errors stayed higher; verb-final agents showed the reverse for OSV and SOV.Source: E ERROR ANALYSES · p. 31
• Agent or patient misidentification accounted for a large portion of errors, with the lower-error dependency pattern reversing between verb-initial and verb-final languages.Source: E ERROR ANALYSES · p. 31
• Under lower speaker capacity and noise, errors declined less strongly and remained higher than baseline, while agent and patient misidentification trends stayed similar.Source: E ERROR ANALYSES · p. 31
• In verb-final lower-capacity runs, only long-dependency utterances were produced from epoch 30, making Figure E5g’s error percentage zero.Source: E ERROR ANALYSES · p. 31
• After communication, seeds in the verb-initial skewed mixed-language noise condition regularized toward either SO or OS order.Source: Figure B2 · p. 32
• Figure D4 compares speaking, listening, and communication performance during learning and communication in the verb-final language across baseline and noise conditions.Source: Figure D4 · p. 33
• Figure E5 tracks short- and long-dependency error percentages across epochs for verb-initial and verb-final languages under three conditions.Source: E ERROR ANALYSES · pp. 31, 33