This study investigates the potential of cross-lingual transfer learning for Named Entity Recognition (NER) between Hindi and Nepali, two languages that, despite their linguistic similarities, face significant disparities in available resources. By leveraging multilingual BERT models, including RemBERT, BERT Multilingual, MuRIL, and DistilBERT Multilingual, the research examines whether pre-training them on a resource-rich language like Hindi can enhance NER performance in a resource-constrained language like Nepali and vice versa. The study conducts experiments in both monolingual and cross-lingual settings to evaluate the models’ effectiveness in transferring linguistic knowledge between the two languages. The findings reveal that while RemBERT and MuRIL perform well in monolingual contexts—RemBERT excelling in Hindi and MuRIL in Nepali—BERT Multilingual performs comparatively best in cross-lingual scenarios, in generalizing features across the languages. Although DistilBERT Multilingual demonstrates slightly lower performance in cross-lingual tasks, it balances efficiency with competitive results. The study underscores the importance of model selection based on linguistic and resource-specific contexts, highlighting that general-purpose models like BERT Multilingual are particularly well-suited for cross-lingual applications.

Recent attempts at behaviour understanding through language grounding have shown that it is possible to automatically generate models for planning problems from textual instructions. One drawback of these approaches is that they either do not make use of the semantic structure behind the model elements identified in the text, or they manually incorporate a collection of concepts with semantic relationships between them. We call this collection of knowledge situation model. The situation model introduces additional context information to the model. It could also potentially reduce the complexity of the planning problem compared to models that do not use situation models. To address this problem, we propose an approach that automatically generates the situation model from textual instructions. The approach is able to identify various hierarchical, spatial, directional, and causal relations. We use the situation model to automatically generate planning problems in a PDDL notation and we show that the situation model reduces the complexity of the PDDL model in terms of number of operators and branching factor compared to planning models that do not make use of situation models.

Different approaches for behaviour understanding rely on textual instructions to generate models of human behaviour. These approaches usually use state of the art parsers to obtain the part of speech (POS) meaning and dependencies of the words in the instructions. For them it is essential that the parser is able to correctly annotate the instructions and especially the verbs as they describe the actions of the person. State of the art parsers usually make errors when annotating textual instructions, as they have short sentence structure often in imperative form. The inability of the parser to identify the verbs results in the inability of behaviour understanding systems to identify the relevant actions. To address this problem, we propose a simple rule-based model that attempts to correct any incorrectly annotated verbs. We argue that the model is able to significantly improve the parser’s performance without the need of additional training data. We evaluate our approach by extracting the actions from 61 textual instructions annotated only with the Stanford parser and once again after applying our model. The results show a significant improvement in the recognition rate when applying the rules (75% accuracy compared to 68% without the rules, p-value < 0.001).