| Challenge: | Hierarchical Multiscale LSTM model learns structure from character input . high complexity of architecture, training and implementations might hinder its applicability . |
| Approach: | They propose to reproduce and ablate hierarchical multiscale LSTM language model and show that simplifying certain aspects of the architecture can improve its performance. |
| Outcome: | The proposed model performs better when simplified and linguistic units are learned by different levels of the model. |
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| Challenge: | Recent work in NLP shows that LSTMs capture compositional structure in language data. |
| Approach: | They propose to measure the decompositional interdependence between word meanings in an LSTM based on their gate interactions. |
| Outcome: | The proposed model can model syntactic relationships rather than learning the longer-range relations independently. |
How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech (2023.acl-long)
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| Challenge: | LSTMs and Transformers perform well at capturing the surface statistics of child-directed speech, but both model types generalize in a way consistent with an incorrect linear rule than the correct hierarchical rule. |
| Approach: | They train LSTMs and Transformers on text from the CHILDES corpus and evaluate what they learn about English yes/no questions. |
| Outcome: | The proposed models perform well at capturing the surface statistics of child-directed speech, but generalize more consistent with an incorrect linear rule than the correct hierarchical rule. |
The emergence of number and syntax units in LSTM language models (N19-1)
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| Challenge: | a recent study shows that LSTMs can capture syntax-sensitive generalizations such as long-distance number agreement. |
| Approach: | They investigate the inner mechanics of number tracking in LSTMs at the single neuron level . they find that long-distance number information is largely managed by two "number units" importantly, the behaviour of these units is partially controlled by other units to track syntactic structure . |
| Outcome: | The proposed model is based on a language model with a long-distance number agreement task. |
Continuous Learning in a Hierarchical Multiscale Neural Network (P18-2)
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| Challenge: | Language models are a major class of natural language processing (NLP) models whose development has led to major progress in many areas like translation, speech recognition or summarization. |
| Approach: | They propose a hierarchical multi-scale language model where short time-scale dependencies are encoded in the hidden state of a lower-level recurrent neural network while longer time- scale dependencies can be encoded into the dynamic of the lower- level network. |
| Outcome: | The proposed model uses a meta-learner to update the weights of the lower-level neural network in an online meta-learning fashion to prevent catastrophic forgetting in the continuous learning framework. |
On the Relation between Linguistic Typology and (Limitations of) Multilingual Language Modeling (D18-1)
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| Challenge: | a key challenge in cross-lingual NLP is developing general language-independent architectures that are equally applicable to any language. |
| Approach: | They propose to use a full-vocabulary setup to test the performance of language modeling (LM) on 50 typologically diverse languages. |
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LSTMs Can Learn Syntax-Sensitive Dependencies Well, But Modeling Structure Makes Them Better (P18-1)
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| Challenge: | a recent study found that language models fail to learn long-range syntax sensitive dependencies. |
| Approach: | They propose to use a subject-verb agreement diagnostic to determine whether language models can learn long-range syntax sensitive dependencies. |
| Outcome: | The proposed model outperforms left-corner and bottom-up variants in learning non-local dependencies. |
Structural Supervision Improves Learning of Non-Local Grammatical Dependencies (N19-1)
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| Challenge: | State-of-the-art LSTM language models learn sequential contingencies with some success . LS models fail to learn other non-local grammatical dependencies, however . |
| Approach: | They compare LSTM language models with RNNGs to examine grammatical dependencies . they find that hierarchical supervision improves learning of non-local dependencies. |
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What is the best recipe for character-level encoder-only modelling? (2023.acl-long)
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| Challenge: | aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. |
| Approach: | They aim to find the best way to build and train character-level BERT-like models by comparing architectural innovations with pretraining objectives. |
| Outcome: | The proposed model outperforms a token-based model on a set of evaluation tasks with a fixed training procedure. |
Morphology Matters: A Multilingual Language Modeling Analysis (2021.tacl-1)
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| Challenge: | Existing studies on inflectional morphology disagree on whether or not it makes languages harder to model. |
| Approach: | They propose to use a corpus of 145 Bible translations in 92 languages to investigate whether inflectional morphology makes languages harder to model. |
| Outcome: | The proposed model trains with linguistically motivated subword segmentation strategies and reduces the impact of morphology on language modeling. |
Model-based analysis of brain activity reveals the hierarchy of language in 305 subjects (2021.findings-emnlp)
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| Challenge: | a popular approach to decompose the neural bases of language requires large and costly data sets to obtain. |
| Approach: | They propose a model-based approach to decompose the neural bases of language that can be used to correlate brain responses to different stimuli. |
| Outcome: | The proposed model-based approach replicates the seminal study of Lerner et al. (2011), which revealed the hierarchy of language areas by comparing the functional-magnetic resonance imaging (fMRI) of seven subjects listening to 7min of both regular and scrambled narratives. |