Challenge: Existing models of language understanding are based on explicit representations of hierarchical structure, but there are good reasons to doubt that they can be said to understand language in any meaningful way.
Approach: They examine whether syntactic and semantic graph representations can complement and improve neural language modeling.
Outcome: The proposed model outperforms pretrained models on English WSJ in perplexity and other metrics.

Similar Papers

Exploiting Syntactic Structure for Better Language Modeling: A Syntactic Distance Approach (2020.acl-main)

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Challenge: incorporating syntactic structure into language models has been a challenge since the 1990s.
Approach: They propose to use syntactic information to integrate syntastic structure into neural language models by providing ground truth parse trees as additional training signals.
Outcome: The proposed model achieves lower perplexity and better quality when ground truth parse trees are provided as training signals.
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)

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Challenge: Several testing methodologies have been developed to probe models’ syntactic representations.
Approach: They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax.
Outcome: The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs.
Graph Pre-training for AMR Parsing and Generation (2022.acl-long)

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Challenge: Abstract meaning representation (AMR) highlights the core semantic information of text in a graph structure.
Approach: They propose two graph auto-encoding strategies for graph-to-graph pre-training and four tasks to integrate text and graph information during pre-tuning to improve structure awareness.
Outcome: The proposed model is superior to pre-trained language models on AMR parsing and AMR-to-text generation tasks.
Infusing Finetuning with Semantic Dependencies (2021.tacl-1)

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Challenge: Several diagnostics help to localize the benefits of our approach.
Approach: They apply convolutional graph encoders to integrate semantic parses into task-specific finetuning.
Outcome: The proposed approach yields benefits to natural language understanding (NLU) tasks in the GLUE benchmark.
Do Neural Language Models Show Preferences for Syntactic Formalisms? (2020.acl-main)

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Challenge: Recent work on interpretability of deep neural language models concludes that many properties of natural language syntax are encoded in their representational spaces.
Approach: They propose to examine whether syntactic structure adheres to a surface-syntactical or deep syntaktic style of analysis.
Outcome: The proposed model prefers Universal Dependencies (UD) over Surface-Syntactic Universal Dependency (SUD) with interesting variations across languages and layers.
Mapping Brains with Language Models: A Survey (2023.findings-acl)

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Challenge: accumulated evidence for brain and language model activations remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism.
Approach: They examine the evidence accumulated by 30 studies spanning 10 datasets and 8 metrics to determine whether there is any overlap between brain and language model activations.
Outcome: The findings suggest that representations extracted from NLP models can (partially) explain the signal found in neural data.
On Efficiently Representing Regular Languages as RNNs (2024.findings-acl)

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Challenge: Recent work by Hewitt et al. (2020) provides an interpretation of the empirical success of recurrent neural networks (RNNs) as language models (LMs).
Approach: They generalize their construction and show that RNNs can efficiently represent a larger class of LMs than previously claimed.
Outcome: The results suggest that RNNs can represent a larger class of LMs than previously claimed .
Implicit Representations of Meaning in Neural Language Models (2021.acl-long)

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Challenge: Neural language models (NLMs) encode lexical relations and syntactic structure, but their effectiveness is still unclear.
Approach: They propose to use text as a model to model entities and situations as they evolve throughout a discourse.
Outcome: The proposed models have functional similarities to linguistic models of dynamic semantics and can be learned with only text as training data.
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)

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Challenge: Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models.
Approach: They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models .
Outcome: The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning.
Do Neural Language Models Inferentially Compose Concepts the Way Humans Can? (2024.lrec-main)

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Challenge: a new study shows that language models and humans may rely on different approaches to represent and compose lexical items across sentence structure.
Approach: They propose to use a dataset to test the performance of neural language models and humans on inferentially driven conceptual compositions.
Outcome: The proposed model elicits probability estimates for a noun in a minimally composed phrase . RoBERTa, BERT-large, and GPT-2 exhibited the closest resemblance to human responses .

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