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KagNet: Knowledge-Aware Graph Networks for Commonsense Reasoning
TLDR
This paper proposes a textual inference framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences.
CommonGen: A Constrained Text Generation Challenge for Generative Commonsense Reasoning
TLDR
A constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning, and demonstrates that the learned generative Commonsense reasoning capability can be transferred to improve downstream tasks such as CommonsenseQA by generating additional context.
Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering
TLDR
A novel knowledge-aware approach that equips pre-trained language models (PTLMs) with a multi-hop relational reasoning module, namedmulti-hop graph relation network (MHGRN), which performs multi-Hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs.
CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP
TLDR
This paper introduces CROSSFIT, a task setup for studying cross-task few-shot learning ability, which standardizes seen/unseen task splits, data access during different learning stages, and the evaluation protocols, and presents NLP Few-shot Gym, a repository of 160 few- Shots tasks, covering diverse task categories and applications, and converted to a unified text-to-text format.
Multi-channel BiLSTM-CRF Model for Emerging Named Entity Recognition in Social Media
TLDR
A novel approach is proposed, which incorporates comprehensive word representations with multi-channel information and Conditional Random Fields (CRF) into a traditional Bidirectional Long Short-Term Memory (BiLSTM) neural network without using any additional hand-craft features such as gazetteers.
NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction
TLDR
A neural approach to ground rules for RE is presented, named Nero, which jointly learns a relation extraction module and a soft matching module that learns to match rules with semantically similar sentences such that raw corpora can be automatically labeled and leveraged by the RE module (in a much better coverage) as augmented supervision.
Pre-training Text-to-Text Transformers for Concept-centric Common Sense
TLDR
It is shown that while only incrementally pre-trained on a relatively small corpus for a few steps, CALM outperforms baseline methods by a consistent margin and even comparable with some larger PTLMs, which suggests that CALM can serve as a general, plug-and-play method for improving the commonsense reasoning ability of a PTLM.
TriggerNER: Learning with Entity Triggers as Explanations for Named Entity Recognition
TLDR
The proposed model, Trigger Matching Network, jointly learns trigger representations and soft matching module with self-attention such that can generalize to unseen sentences easily for tagging, and is significantly more cost-effective than the traditional neural NER frameworks.
Birds Have Four Legs?! NumerSense: Probing Numerical Commonsense Knowledge of Pre-trained Language Models
TLDR
Investigating whether and to what extent one can induce numerical commonsense knowledge from PTLMs as well as the robustness of this process finds that this may not work for numerical Commonsense knowledge.
Can BERT Reason? Logically Equivalent Probes for Evaluating the Inference Capabilities of Language Models
TLDR
It is found that despite the recent success of large PTLMs on commonsense benchmarks, their performances on probes are no better than random guessing (even with fine-tuning) and are heavily dependent on biases--the poor overall performance inhibits us from studying robustness.
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