Papers with CRFs
Hybrid semi-Markov CRF for Neural Sequence Labeling (P18-2)
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| Challenge: | Existing conditional random fields (CRFs) use hand-crafted features to perform sequence labeling tasks. |
| Approach: | They propose to use semi-Markov conditional random fields for neural sequence labeling in natural language processing to extract features from segments instead of words. |
| Outcome: | The proposed model achieves state-of-the-art when no external knowledge is used. |
Semantic Frame Parsing for Information Extraction : the CALOR corpus (L18-1)
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| Challenge: | a recent study compares the semantic parsing of encyclopedic history texts with the Berkeley FrameNet project. |
| Approach: | They propose to use Berkeley FrameNet to parse encyclopedic history texts . they use a sequence labeling model which optimizes frame identification and role segmentation . |
| Outcome: | The proposed approach leverages the manual annotation of larger corpora than full text parsing. |
Linguistically-driven Framework for Computationally Efficient and Scalable Sign Recognition (L18-1)
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| Challenge: | a new general framework for sign recognition from monocular video is presented . the framework exploits state-of-the-art learning methods while incorporating features based on what we know about the linguistic composition of lexical signs. |
| Approach: | They propose a general framework for sign recognition from monocular video . they exploit state-of-the-art learning methods while incorporating features from linguistic information . |
| Outcome: | The proposed framework exploits state-of-the-art learning methods while incorporating features based on what we know about linguistic composition of lexical signs. |
Pretrained Language Models for Sequential Sentence Classification (D19-1)
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| Challenge: | Recent successful models for document-level understanding have used hierarchical encoding and CRFs to capture dependencies between subsequent labels. |
| Approach: | They propose a pretrained language model that captures contextual dependencies without hierarchical encoding nor a CRF. |
| Outcome: | The proposed model captures contextual dependencies without hierarchical encoding nor a CRF on four datasets, including a new dataset of structured scientific abstracts. |
Dissecting Span Identification Tasks with Performance Prediction (2020.emnlp-main)
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| Challenge: | Span identification tasks are a staple of applied NLP, but there is little insight on how their properties influence their difficulty. |
| Approach: | They propose to build a model to predict span ID performance for unseen span ID tasks that can support architecture choices. |
| Outcome: | The proposed model predicts span ID tasks for unseen span ID task in English, and the meta model predictable span ID performance. |
Training for Gibbs Sampling on Conditional Random Fields with Neural Scoring Factors (2020.emnlp-main)
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| Challenge: | Recent advances in NLP focus on simple approaches to model the output label space . graphical models are often limited to (heuristic) greedy search and its variants . |
| Approach: | They propose an approach for efficiently training and decoding hybrids of graphical and graphical models based on Gibbs sampling. |
| Outcome: | The proposed approach improves on Dutch and Dutch with graphical models . the proposed model improves over a strong baseline on three languages . |
Phrase Grounding by Soft-Label Chain Conditional Random Field (D19-1)
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| Challenge: | Existing methods to ground entities depend on inference or non-differentiable losses. |
| Approach: | They propose a phrase grounding task that grounds entities to corresponding regions in an image . they use neural chain Conditional Random Fields to model dependencies among regions . |
| Outcome: | The proposed method is based on a dataset of the Flickr30k Entities dataset. |
Algorithms for Acyclic Weighted Finite-State Automata with Failure Arcs (2022.emnlp-main)
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| Challenge: | Weighted finite-state automata (WSFAs) are common formalisms in NLP. |
| Approach: | They propose an algorithm for semiring-weighted WFSAs with av-erage out symbol fractions . |
| Outcome: | The proposed algorithms are faster than the standard methods for weighted finite-state automata. |
Chunk Different Kind of Spoken Discourse: Challenges for Machine Learning (2020.lrec-1)
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| Challenge: | Existing chunkers for spoken data are based on a corpus composed of monologues and spontaneous talk in interaction. |
| Approach: | They propose to use CRFs to develop a chunker for spoken data . the chunker is based on a small corpus composed of two kinds of discourse . |
| Outcome: | The proposed chunker is based on a spoken corpus composed of monologue and spontaneous talk in interaction. |
Addressing NER Annotation Noises with Uncertainty-Guided Tree-Structured CRFs (2023.emnlp-main)
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| Challenge: | Named entity recognition datasets are notorious for their noisy nature due to annotation errors, inconsistencies, and subjective interpretations. |
| Approach: | They propose a method that considers NER as a constituency tree parsing problem and uses a tree-structured Conditional Random Fields with uncertainty evaluation for integration. |
| Outcome: | The proposed model exhibits superb performance even in extreme scenarios with 90% annotation noise. |