Papers with syntactic processing
SentSpace: Large-Scale Benchmarking and Evaluation of Text using Cognitively Motivated Lexical, Syntactic, and Semantic Features (2022.naacl-demo)
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| Challenge: | SentSpace provides a framework for streamlined evaluation of textual input. |
| Approach: | They describe the design of SentSpace and demonstrate an example use case . they use a web interface for interactive visualization and comparison with large corpora . |
| Outcome: | The framework provides a common framework for evaluation and visualization. |
Specialization through Collaboration: Understanding Expert Interaction in Mixture-of-Expert Large Language Models (2026.eacl-long)
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| Challenge: | Mixture-of-Experts (MoE) based large language models are popular for multitasking . however, whether each expert can specialize to a task remains unclear . |
| Approach: | They propose to use a dictionary learning approach to analyze expert collaboration mechanisms in MoE LLMs. |
| Outcome: | The proposed model outperforms existing methods by 2.5% while enabling 50% expert reduction. |
Finding syntax in human encephalography with beam search (P18-1)
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| Challenge: | RNNGs are generative models of (tree , string ) pairs that evaluate derivational choices . a non-syntactic neural language model yields no reliable effects . |
| Approach: | They propose to combine a probabilistic generative grammar with a parsing procedure that uses it to manage syntactic derivations as it advances from one word to the next. |
| Outcome: | The proposed model derives two amplitude effects when used against human encephalography data. |
Word Segmentation as Unsupervised Constituency Parsing (2022.acl-long)
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| Challenge: | Existing theories of word identification from continuous inputs are based on statistical cues, such as Bayesian inference and normative statistics. |
| Approach: | They propose a model which allows for a process isomorphic to unsupervised constituency parsing and which can reproduce human behavior in word identification experiments. |
| Outcome: | The proposed model reproduces human behavior in word identification experiments, suggesting it is viable to study word identification and its relation to syntactic processing. |
Can Transformers Process Recursive Nested Constructions, Like Humans? (2022.coling-1)
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| Challenge: | A recent study evaluated recursive processing in recurrent neural language models (RNN-LMs) and showed that such models perform below chance level on embedded dependencies within nested constructions. |
| Approach: | They evaluated recursive processing in recurrent neural language models and found that Transformers perform below chance level on embedded dependencies within nested constructions. |
| Outcome: | The proposed models perform below chance level on embedded dependencies within nested constructions, compared to humans. |
Syntactic Scaffolds for Semantic Structures (D18-1)
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| Challenge: | Syntactic scaffolds avoid expensive syntactical processing at runtime . many systems have used syntastic dependency or phrase-based parsers as preprocessing for semantic analysis. |
| Approach: | They propose a multitask learning approach that uses a syntactic treebank to integrate syntaktic information into semantic tasks. |
| Outcome: | The proposed method improves on PropBank semantics, frame semantics and coreference resolution tasks. |