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Found 18 papers, 10 papers with code
Multi-Level Gazetteer-Free Geocoding
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic coordinates.
Open-Domain Contextual Link Prediction and its Complementarity with Entailment Graphs
In this paper, we introduce the new task of open-domain contextual link prediction which has access to both the textual context and the KG structure to perform link prediction.
Scalable and Domain-General Abstractive Proposition Segmentation
We model proposition segmentation as a supervised task by training LLMs on existing annotated datasets and show that training yields significantly improved results.
A synthetic data approach for domain generalization of NLI models
We also demonstrate gains on test sets when in-domain training data is augmented with our domain-general synthetic data.
LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction
Transformer encoders contextualize token representations by attending to all other tokens at each layer, leading to quadratic increase in compute effort with the input length.
Sources of Hallucination by Large Language Models on Inference Tasks
Large Language Models (LLMs) are claimed to be capable of Natural Language Inference (NLI), necessary for applied tasks like question answering and summarization.
Resolving Indirect Referring Expressions for Entity Selection
We address this problem of reference resolution, when people use natural expressions to choose between the entities.
Language Models Are Poor Learners of Directional Inference
We examine LMs' competence of directional predicate entailments by supervised fine-tuning with prompts.
Cross-lingual Inference with A Chinese Entailment Graph
Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples.
Incorporating Temporal Information in Entailment Graph Mining
We present a novel method for injecting temporality into entailment graphs to address the problem of spurious entailments, which may arise from similar but temporally distinct events involving the same pair of entities.
Multivalent Entailment Graphs for Question Answering
Drawing inferences between open-domain natural language predicates is a necessity for true language understanding.
Spatial Language Representation with Multi-Level Geocoding
We present a multi-level geocoding model (MLG) that learns to associate texts to geographic locations.
Jointly Modeling Hierarchical and Horizontal Features for Relational Triple Extraction
In this work, we first introduce the hierarchical dependency and horizontal commonality between the two levels, and then propose an entity-enhanced dual tagging framework that enables the triple extraction (TE) task to utilize such interactions with self-learned entity features through an auxiliary entity extraction (EE) task, without breaking the joint decoding of relational triples.
Duality of Link Prediction and Entailment Graph Induction
The new entailment score outperforms prior state-of-the-art results on a standard entialment dataset and the new link prediction scores show improvements over the raw link prediction scores.
Learning Typed Entailment Graphs with Global Soft Constraints
We instead propose a scalable method that learns globally consistent similarity scores based on new soft constraints that consider both the structures across typed entailment graphs and inside each graph.
Learning Sparse Gaussian Graphical Models with Overlapping Blocks
3) It can jointly learn a network structure and overlapping blocks.
