Modeling Language Learning in Corrective Feedback Interactions (2025)

Abstract: To study computational models for language acquisition, we propose an interactive computational framework that utilizes a miniature language acquisition dataset in a controlled environment. In this framework, a neural learner model interacts with a teacher model that provides corrective feedback. Within this framework, we investigate various corrective feedback strategies, specifically focusing on reformulations and their effect on the learner model during their interactions. We design experimental settings to evaluate the learner’s production of syntactically and semantically correct linguistic utterances and perception of concepts and word-meaning associations.These results offer insights into the effectiveness of different feedback strategies in language acquisition using artificial neural networks. The outcome of this research is establishing a framework with a dataset for the systematic evaluation of various aspects of language acquisition in a controlled environment.

## introduction understanding how children form associations between linguistic words to some situational input or referent within an uncertain environment where multiple referents could be related to the same word is a topic that has been studied in language acquisition @xcite . cross-situational learning is a powerful mechanism for learning co-occurrence statistics between words and referent objects across multiple exposures @xcite . studies of cross-situational learning in both adults @xcite and children @xcite show how the association between words and meaning is learned at different stages of language development. however, some of these studies on cross-situational learning focus on the child's learning of these word-meaning associations without any form of feedback @xcite . feedback, in the form of social interactions, is shown to enhance children's language development @xcite @xcite . in language acquisition studies, most commonly in second language acquisition literature, an interaction is viewed as a negotiation for meaning where two agents "negotiate" or agree upon the meaning of some object during a conversation @xcite . corrective feedback is one form of interaction where an adult, i.e. parent or teacher, analyzes the linguistic generation of a child and provides some form of response intended to adjust or update the child's linguistic knowledge. although the impact of providing corrective feedback is a controversial topic, many studies supports its influence on language learning even in first language acquisition @xcite @xcite . in a social context, commonly within a classroom setting, several approaches for corrective feedback are utilized such as explicit correction, recast or reformulations, clarification request, metalinguistic feedback, elicitation, and repetition @xcite . for the scope of this paper, we will focus on reformulations as our computational approach to corrective feedback. cross-situational learning has been used to address multiple tasks like probabilistic word-meaning learning with symbolic situation representations @xcite , word-meaning learning with embodied systems @xcite , and word-meaning associations from visual perceptual representations as inputs @xcite . also, several studies have explored learning settings that simulate interactions between teacher and learner conversations with k-nearest neighbor models for word learning @xcite and probabilistic models with corrections @xcite . other models have studied the acquisition of semantic knowledge through some form of feedback provided via a reward function in a reinforcement learning setting @xcite . other studies have explored the language acquisition process as a game where two agents observe referents in a scene and both attempt to name it @xcite . building neural computational models for language acquisition based has been practiced in the related literature @xcite . however, one issue that these models present is the requirement of large amount of linguistic data, most times larger than what humans are exposed to through out their entire lifetime. also, the architecture design of these models can have innate biases that makes relating their outcome analysis to human language acquisition theories challenging @xcite . in order to establish a fair and accurate comparison between neural models and human learners, it is required to simulate learning scenarios where the quantity of data, input modalities, and data distributions resemble humanlevel abilities @xcite . in this paper, we use a learning scenario that explores neural computational models for language acquisition in a teacher-learner interactive cross-situational learning framework. this framework follows a similar structure as the one presented in @xcite which uses a miniature language acquisition dataset where the environment contains logical representation of objects and the model learns to generate linguistic utterances that describe these objects. we extend their work by using recurrent neural networks as the learner rather than a probabilistic graphical model. we train small models from scratch, without any prior knowledge, that resembles early stages of concept learning and language acquisition. also, we examine various corrective feedback strategies and their impact on the learner model's learning trajectory. the learner is evaluated at a production level based on utterance semantics and its ability to generate all possible explanations, as well as a perception level, referring to the knowledge acquired about concepts and relationships. figure 1 shows how interaction is established, where each interaction starts with a learner model generating syntactically and semantically appropriate utterances corresponding to the attributes and relationships within a given situation, represented formally in a formal predicate-argument form. the teacher then analyzes the utterance, compares the utterance's formal representation to the situation's formal representation, and provides another utterance with a similar formal representation to the situation. the main contributions of this work are as follows: 1) extending miniature language acquisition computational framework with neural learner models; 2) providing evaluation datasets and metrics for utterance generation (production-level) and concept evaluation (perception-level); 3) incorporating various corrective feedback strategies in the form of interactions with an oracle that evaluates the logical semantics. ## miniature language acquisition dataset miniature language acquisition (mila) is a task that consists on the learning of a natural language from sentence-picture pairs, where each "picture" or scene is composed of geometric shapes with different properties @xcite . similar to the setup in (angluin and becerra-bonache, 2017), we create a learning setting in which the learner receives a formal representation of the environment, instead of actual visual input, and generates natural language utterances that explain the environment. in our experiments, we only use english natural language utterances. the dataset includes pairs of formal representations of various situations with their respective set of valid linguistic utterances. there are 23,328 unique situations, where each situation refers to two objects with all three attributes and relates to 113,064 unique utterances. every situation is paired with at most 40 utterances. ## language acquisition setup the language acquisition framework presented here aims to evaluate the influence of corrective feedback in the development of a learner model's capacity to describe various situations as well as its association of figure 3 : meaning transducer t . this is an example of the transducer which generates the formal representation of any utterance from the grammar. ## experimental settings all learner models were implemented using pytorch @xcite . the encoder and decoder grus have a hidden size of 300. the situation inputs are passed into a one-hot embedding layer forming a context vector representation. we used the adam optimizer with a learning rate of 0.001. all learner models are trained with 18,000 situation-utterance set pairs and evaluated with 5,000 pairs. the learner's evaluation performance is recorded every 500 interactions. ## results syntactic errors. our model does not show any significant amount of syntax errors after a few interactions. figure 5 shows the number of utterances that were classified as "syntax error" by the teacher's evaluation module for all models during the first 200 interactions. after 30 interactions, we see that corrective random feedback, non-corrective random feedback and non-corrective lexical distance have all of its generated utterances during the evaluation classified as "syntax error". later interactions, we see that syntax errors become non-existent because the model is able to generate utterances that follow the expected grammar. each learner model is initialized with the same random seed to ensure the comparison on the effects of each feedback strategy are from the same starting point. this high syntax error after 30 interactions present a point where the models were starting to get utterances with repeated shape concepts at different position before learning that size and color concepts occur before shape concepts, thus adjusting to the grammar. semantic accuracy. figure 6 shows the learner's semantic accuracy for each feedback strategy and feedback frequency. it is observed that corrective random feedback and corrective lexical distance has higher semantic accuracy than all other models while noncorrective and corrective full-length feedback have the lowest semantic accuracy. there were cases where the learner model described the situation using one relation while the teacher provided a valid feedback with the opposite relation. this causes confusion to the model due to misaligned objects. for example, the learner generated the utterance "the big red star to the left of the small yellow circle" and the teacher provided "the small yellow circle to the right of the big red star". corrective random feedback and lexical distance shows oscillations between interactions. we observed that these feedback strategies provide utterances of different lengths more frequent than full-length feedback which causes the learner model to fail to describe some situations. as shown in the learner trained with lexical distance feedback strategy correctly generate valid utterances earlier than other strategies because these generated utterances only describe the shape of at least one object in the situation. on the other hand, full-length feedback expects the learner model to describe the size, color, and shape of both objects which requires more interactions for the learner to successfully describe a situation following this strategy. all feedback strategies with corrective frequency are able to reach each performance threshold with less interaction than non-corrective frequency. this is interesting because it shows that for this task, limiting the amount of corrective feedback helps the learner generate more valid utterances for each situation. completeness. figure 7 shows the learner completeness score at various interactions between the models. it shows that non-corrective feedback strategies have higher scores than corrective feedback strategies. noncorrective random feedback has the highest completeness score at 55%, which means that the learner model trained with this strategy and frequency was able to generate around 20 different valid utterances. on the other hand, corrective full-length feedback only generated 3 valid utterances which is approximately 7%. when comparing the completeness score and the semantic accuracy, there is no indication that high semantic accuracy also means high completeness. instead, we observed that full-length feedback had low semantic accuracy and low completeness score. also, both random feedback and lexical distance feedback had high semantic accuracy and high completeness. a possible explanation for this lies on the diversity of feedback utterances the learner receives. random feedback provides utterances of different lengths whereas full-length feedback only provides one or two utterances with the full description of the situation. we can also argue that lexical distance feedback is in the middle in terms of performance because this strategy selected utterances with minimal edit-distance to the learner's utterance, thus limiting the range of possible utterances. we believe that exposing the learner to different utterances are helpful for the learner model to develop its set of utterances to describe each situation. ## discussion in the production setting, each feedback strategy presents forms in which a teacher could provide feedback to a learner as guided responses, from describing the full situation to rephrasing the utterance to correct incorrect or missing concepts. the learner models had a positive response to these guided types of responses which yielded higher semantic accuracy. however, we noticed that each model generated utterances of specific concept combinations. this is also supported by the completeness evaluation which shows the set of utterances the model is able to associate to each situation. random feedback strategy generated utterances of different length for each situation. these results indicate that exposing the learner model to various feedback utterances across multiple situations can improve the model's semantic accuracy as well as completeness score. we analyzed aggregated attention maps during the model's production setting to measure the wordmeaning mappings were developed as new interactions occurred. the maps for full-length feedback show that this strategy help the learner model have strong oneto-one association between concept words and their respective predicates. for random feedback and lexical distance feedback strategies, the models formed associations between each word to multiple predicates corresponding to different concepts. further details and attention maps can be found in appendix a in addition to analyze attention maps, we observed the error rates of every concept for each feedback strategy. overall, the error rates for each concept decreases as the learner interacts with the teacher. some strategies like random and lexical distance feedback have higher error rates at earlier interactions, they still decrease during later interactions. additional details are in appendix b although the evaluation metrics in our experiments aim to explore the acquisition of a natural language by neural models, they are not sufficient and thus more complex metrics are required. for a fair comparison between neural language acquisition and human language acquisition, establishing cognitive plausible neural architectures. by cognitive plausible, we mean that neural models could emulate human-like processing @xcite . we believe that this framework has potential to include additional metrics that can evaluate neural model learning and gain insights to language acquisition theories. ## conclusion this work proposes a framework to explore teacherlearner interactions with corrective feedback within a controlled environment using formal representations of objects and their properties. we evaluate various feedback strategies and their influence on the learner model's utterance generation for a given situation and perception of different concepts like shape, color, size, and relation present in a situation. these results show that the learner models can generate a different subset of valid utterances to describe a situation according to the feedback strategy employed by the teacher. some strategies like random and lexical distance were useful for the learner model to learn multiple utterances whereas full-length only allowed the learner model to learn 1-2 valid utterances. in terms of perception, some models present challenges for certain concepts due to unseen target utterances during concept evaluation that were not provided by the teacher during training inter-actions. given the reduced size of the vocabulary and the concepts, the use of gru was selected to better highlight the main idea of creating a controlled framework that could be insightful, in terms of production and perception of a synthetic language, for the community to study the challenges of language acquisition and in-depth semantic evaluation of generated utterances. further study and technical developments are needed to possibly incorporate newer neural models into this framework. in this paper, the teacher's feedback served as the ground truth labels used by the learner model. the learner model did not do any analysis or additional processing of the teacher's feedback which might limit the effectiveness of feedback and interactions for future work, we need to design procedures where the learner model can interpret the teacher's feedback and use said interpretation in the production process. one approach is to introduce mechanisms that allows the learner model to use the formal representation of the teacher's utterance to incorporate semantics into the loss function calculations.