Papers with natural language sentences
SKILL: Structured Knowledge Infusion for Large Language Models (2022.naacl-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) have demonstrated human-level performance on a vast spectrum of natural language tasks. |
| Approach: | They propose a method to infuse structured knowledge into large language models by directly training T5 models on factual triples of knowledge graphs (KGs). |
| Outcome: | The proposed method outperforms baseline models on FreebaseQA and WikiHop, as well as the Wikidata-answerable subset of TriviaQA and NaturalQuestions. |
LAGr: Label Aligned Graphs for Better Systematic Generalization in Semantic Parsing (2022.acl-long)
Copied to clipboard
| Challenge: | Semantic parsers struggle to generalize to examples with unseen combinations of seen rules from the training set. |
| Approach: | They propose a general framework to produce semantic parses by predicting node labels for a complete multi-layer input-aligned graph. |
| Outcome: | The proposed framework produces better generalizations than the baseline framework . it produces representations directly as a graph and not as sequences . |
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction (2020.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for relation extraction use heuristics or distant-supervised annotations, but distant supervised methods make strong assumptions on entity cooccurrence without sufficient contexts. |
| Approach: | They propose a framework that exploits weak, self-supervised signals by leveraging large pretrained language models for adaptive clustering on contextualized relational features. |
| Outcome: | The proposed framework exploits weak, self-supervised signals on open-domain Relation Extraction . it bootstraps the self-supervised signals by improving contextualized features in relation classification . |
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing fact extraction and verification tasks only consider evidence of a single format . Existing models convert evidence into either sentences or tables, thus losing context information . |
| Approach: | They propose a Dual Channel Unified Format fact verification model which unifies various evidence into parallel streams, i.e., natural language sentences and a global evidence table, simultaneously. |
| Outcome: | The proposed model outperforms existing models in two formats by a large margin . it makes the most of existing tables and tables to absorb evidence of two formats . |
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction (2022.naacl-main)
Copied to clipboard
| Challenge: | Existing methods to extract relational feature signals from natural language sentences use self-supervised clustering and classification that cause gradual drift problems. |
| Approach: | They propose a framework that derives hierarchical signals from relational feature space using cross hierarchy attention and effectively optimizes relation representation of sentences under exemplar-wise contrastive learning. |
| Outcome: | The proposed framework can extract the relationship between entities from natural language sentences without prior knowledge on relation scope or distribution. |
MILIE: Modular & Iterative Multilingual Open Information Extraction (2022.acl-long)
Copied to clipboard
Bhushan Kotnis, Kiril Gashteovski, Daniel Rubio, Ammar Shaker, Vanesa Rodriguez-Tembras, Makoto Takamoto, Mathias Niepert, Carolin Lawrence
| Challenge: | Current OpenIE systems extract all triple slots independently. |
| Approach: | They propose a neural OpenIE system that extracts triple slots iteratively . they propose to use the system to extract easy slots and difficult ones . |
| Outcome: | The proposed system outperforms SOTA systems on multiple languages ranging from Chinese to Arabic. |
BiBL: AMR Parsing and Generation with Bidirectional Bayesian Learning (2022.coling-1)
Copied to clipboard
| Challenge: | Existing approaches to AMR focus on one-side improvements despite the duality of the two tasks . instead, we propose data-efficient Bidirectional Bayesian learning (BiBL) to facilitate bidirectional information transition. |
| Approach: | They propose a data-efficient bidirectional Bayesian learning approach to facilitate bidirectional information transition by adopting a single-stage multitasking strategy. |
| Outcome: | The proposed model outperforms existing models on benchmark datasets without extra training data. |
Improving Open Information Extraction via Iterative Rank-Aware Learning (P19-1)
Copied to clipboard
| Challenge: | Open information extraction (IE) is the task of extracting open-domain assertions from natural language sentences. |
| Approach: | They propose an additional binary classification loss to calibrate the extraction likelihood . they propose an iterative learning process where extractions generated by the open IE model are incrementally included as training samples to help the model learn from trial and error. |
| Outcome: | Experiments on open information extraction (IE) show that the extraction likelihood is not well calibrated when comparing quality of extracted assertions. |
Capturing Argument Interaction in Semantic Role Labeling with Capsule Networks (D19-1)
Copied to clipboard
| Challenge: | State-of-the-art SRL models do not model non-local interaction between arguments . e.g., LSTMs do not allow for efficient inference . |
| Approach: | They propose a new approach to model interactions between arguments using capsule networks . they analyze errors in the refinement procedure by capturing intuition in a flexible way . |
| Outcome: | The proposed model outperforms the baseline model on all 7 languages and achieves state-of-the-art results on 5 languages including English. |