Some people can mix two languages within the same sentence: this is known as intra-sentential code-switching. Themajority of computational models on language comprehension are dedicated to one language. Some bilingual modelshave also been developed, but very few have explored the code-switching case. We collected data from human subjectsthat were required to mix pairs of given sentences in French and English. Truly bilingual subjects produced more switcheswithin the same sentence. The corpus obtained have some very complex mixed sentences: there can be until elevenlanguage switches within the same sentence. Then, we trained ResPars, a Reservoir-based sentence Parsing model, withthe collected corpus. This Recurrent Neural Network model processes sentences incrementally, word by word, and outputsthe sentence meaning (i.e. thematic roles). Surprisingly the model is able to learn and generalize on the mixed corpus withperformances nearly as good as the unmixed French-English corpus.

Several popular sequence-based and pretrained language models have been found to be successful for text-driven prediction of brain activations. However, these models still lack long-term cognitive plausibility as well as insights on the underlying neural substrate mechanisms. This paper studies the influence of context representations of different language models such as sequence-based models: Long short-term memory networks (LSTMs) and ELMo, and popular pretrained Transformer language model (Longformer). In particular, we study how the internal hidden representations align with the brain activity observed via fMRI when the subjects listen to several narrative stories. We use brain imaging recordings of subjects listening to narrative stories to interpret word and sequence embeddings. We further investigate how the representations of language models layers reveal better semantic context during listening. Experiments across all language model representations provide the following cognitive insights: (i) the representations of LSTM cell states are better aligned with brain recordings than LSTM (hidden state), the cell state activity can represent more long-term information, (ii) the representations of ELMo and Longformer display a good predictive performance across brain regions for listening stimuli; (iii) Posterior Medial Cortex (PMC), Temporo-Parieto-Occipital junction (TPOJ), and Dorsal Frontal Lobe (DFL) have higher correlation versus Early Auditory (EAC) and Auditory Association Cortex (AAC).

How do children bootstrap language through noisy supervision? Most prior works focused on tracking co-occurrences between individual words and referents. We model cross-situational learning (CSL) at sentence level with few (1000) training examples. We compare reservoir computing (RC) and LSTMs on three datasets including complex robotic commands. For most experiments, reservoirs yield superior performance over LSTMs. Surprisingly, reservoirs demonstrate robust generalization when increasing vocabulary size: the error grows slowly. On the contrary, LSTMs are not robust: the number of hidden units needs to be dramatically increased to follow up vocabulary size increase, which is questionable from a biological or cognitive perspective. This suggests that that random projections used in RC helps to bootstrap generalization quickly. To our knowledge, this is a new result in developmental learning modelling. We analyse the evolution of internal representations during training of both recurrent networks and suggest why reservoir generalization seems more efficient.

Although sentence comprehension and production are increasingly often studied by combining computational modeling and human experiments, this approach remains mostly restricted to studies of monolingual or first-language (L1) processing. There are currently only very few sentence-level computational models of second-language (L2) or bilingual processing (Frank, 2021). This lack of computational specifications can hamper further progress in bilingualism research. Moreover, better understanding of bilingual processing will give more insights into more general mechanisms such as cognitive control processes involved while switching languages (Luk et al., 2012). Our symposium aims to bring together researchers from different labs and with different research traditions, working on the intersection of models and experiments in bilingual sentence processing. The symposium has four talks, by Edith Kaan (associate professor, specializing in psycholinguistics of bilingualism), Yung Han Khoe (PhD student, working on models of bilingual sentence production), Lin Chen (research associate with an expertise in reading processes), and a joint talk by Irene Winther (PhD student working on bilingual sentence processing) and Yevgen Matusevych (research associate in computational cognitive science of language). Finally, we will have a panel discussion to suggest how models could be challenged by experimental data, and provide new explanatory mechanisms. This discussion will be moderated by Xavier Hinaut (research scientist in computational neuroscience) and Stefan Frank (associate professor in computational psycholinguistics).

Computer science offers a large set of tools for prototyping, writing, running, testing, validating, sharing and reproducing results; however, computational science lags behind. In the best case, authors may provide their source code as a compressed archive and they may feel confident their research is reproducible. But this is not exactly true. James Buckheit and David Donoho proposed more than two decades ago that an article about computational results is advertising, not scholarship. The actual scholarship is the full software environment, code, and data that produced the result. This implies new workflows, in particular in peer-reviews. Existing journals have been slow to adapt: source codes are rarely requested and are hardly ever actually executed to check that they produce the results advertised in the article. ReScience is a peer-reviewed journal that targets computational research and encourages the explicit replication of already published research, promoting new and open-source implementations in order to ensure that the original research can be replicated from its description. To achieve this goal, the whole publishing chain is radically different from other traditional scientific journals. ReScience resides on GitHub where each new implementation of a computational study is made available together with comments, explanations, and software tests.