Papers by Mark Anderson
Parsing linearizations appreciate PoS tags - but some are fussy about errors (2022.aacl-short)
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| Challenge: | Recent work on the impact of PoS tags on graph- and transition-based parsers suggests that they are only useful when tagging accuracy is prohibitively high or in low-resource scenarios. |
| Approach: | They examine the impact of PoS tags on graph- and transition-based parsers and propose to use them in a new paradigm for sequence labeling. |
| Outcome: | The proposed model is best when tagging accuracy and resource availability are high. |
John praised Mary because _he_? Implicit Causality Bias and Its Interaction with Explicit Cues in LMs (2021.findings-acl)
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| Challenge: | Psycholinguists have identified one such cue in the implicit causality bias of interpersonal verbs. |
| Approach: | They propose to use pre-trained language models to encode IC bias at inference time . they hypothesize that when a cause is explicitly stated, an incongruent IC biased leads to a delay in human processing. |
| Outcome: | The results suggest that pre-trained language models tend to prioritize lexical patterns over higher-order signals. |
Disfluency Detection using Auto-Correlational Neural Networks (D18-1)
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| Challenge: | a recent study proposes an auto-correlational neural network (ACNN) that can detect disfluency in speech . the model uses a convolutional neural system and augments it with a new auto-corrector . |
| Approach: | They propose a convolutional neural network model that captures "rough copy" dependencies . the model is based on a new auto-correlation operator that capture the kinds of "rough copies" dependency . |
| Outcome: | The proposed model outperforms the baseline CNN on a disfluency detection task with a 5% increase in f-score. |
Assessing the Limits of the Distributional Hypothesis in Semantic Spaces: Trait-based Relational Knowledge and the Impact of Co-occurrences (2022.starsem-1)
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| Challenge: | a rise in performance in NLP has led to a decrease in interpretability . a recent study examined how neural semantic models capture relational knowledge . |
| Approach: | They evaluate how well English and Spanish semantic spaces capture a particular type of relational knowledge . they also explore the role of co-occurrences in this context . |
| Outcome: | The proposed model can be used to predict traits associated with concepts in English and Spanish. |
Replicating and Extending “Because Their Treebanks Leak”: Graph Isomorphism, Covariants, and Parser Performance (2021.acl-short)
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| Challenge: | a small sample size and unreliable results suggest a correlation between parser performance and graph isomorphism is not observed in the wild. |
| Approach: | They propose to replicate a study which found graph isomorphism is a non-trivial variable . they also bin sentences by length and find correlation between parser performance and isopathism disappears . |
| Outcome: | The results show that the original analysis was unreliable and had methodological issues . the study also bin sentences by length and shows that the correlation between parser performance and graph isomorphism disappears when controlling for covariants. |
Predicting accuracy on large datasets from smaller pilot data (P18-2)
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| Challenge: | obtaining training data is often the most difficult part of an NLP or ML project . obtaining data is important to estimate how much training data a system will require to achieve a target accuracy. |
| Approach: | They propose a performance extrapolation task to evaluate extrapolations on larger training sets. |
| Outcome: | The proposed method can predict accuracy on larger training datasets. |
Inherent Dependency Displacement Bias of Transition-Based Algorithms (2020.lrec-1)
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| Challenge: | Empirical studies have shown that performance varies across different treebanks in such a way that one algorithm outperforms another on one treebank and the reverse is true for a different tree bank. |
| Approach: | They introduce the concept of an algorithm’s inherent dependency displacement distribution and characterise its bias in terms of dependency displacement. |
| Outcome: | The proposed model shows that the similarity of an algorithm’s inherent dependency displacement distribution to a treebank’s displacement distribution is clearly correlated to the algorithm’ s parsing performance on that treebank. |