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Found 15 papers, 2 papers with code
FRAME: Evaluating Rationale-Label Consistency Metrics for Free-Text Rationales
Following how humans communicate, free-text rationales aim to use natural language to explain neural language model (LM) behavior.
UNIREX: A Unified Learning Framework for Language Model Rationale Extraction
An extractive rationale explains a language model's (LM's) prediction on a given task instance by highlighting the text inputs that most influenced the prediction.
Exploring Deep Multimodal Fusion of Text and Photo for Hate Speech Classification
Interactions among users on social network platforms are usually positive, constructive and insightful.
Ordered Tree Decomposition for HRG Rule Extraction
Our algorithms are based on finding a tree decomposition of smallest width, relative to the vertex order, and then extracting one rule for each node in this structure.
Neural Models of Text Normalization for Speech Applications
One problem that has been somewhat resistant to effective machine learning solutions is text normalization for speech applications such as text-to-speech synthesis (TTS).
Sequence-to-sequence Models for Cache Transition Systems
In this paper, we present a sequence-to-sequence based approach for mapping natural language sentences to AMR semantic graphs.
Cache Transition Systems for Graph Parsing
Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree.
Addressing the Data Sparsity Issue in Neural AMR Parsing
Neural attention models have achieved great success in different NLP tasks.
AMR-to-text Generation with Synchronous Node Replacement Grammar
This paper addresses the task of AMR-to-text generation by leveraging synchronous node replacement grammar.
AMR-to-text generation as a Traveling Salesman Problem
The task of AMR-to-text generation is to generate grammatical text that sustains the semantic meaning for a given AMR graph.
Exploring phrase-compositionality in skip-gram models
In this paper, we introduce a variation of the skip-gram model which jointly learns distributed word vector representations and their way of composing to form phrase embeddings.
