Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection (D18-1)
Copied to clipboard
| Challenge: | Recent NLP approaches that model relations between text use complex architectures and attention. |
| Approach: | They propose to use labelled data to model semantic relations between two pieces of text . they use word representations to encode matching features directly in the word representation . |
| Outcome: | The proposed approach beats tree kernel models and neural models with similar input encodings while keeping the model simple and fast to train. |
Similar Papers
How to represent a word and predict it, too: Improving tied architectures for language modelling (D18-1)
Copied to clipboard
| Challenge: | Recent state-of-the-art models use word embeddings as input and output mappings instead of tied models. |
| Approach: | They propose to decouple hidden state from word embedding prediction . they extend their proposed modification to word2vec models . |
| Outcome: | The proposed architectures achieve comparable or better results compared to previous models without tying . the proposed architecture reduces parameters, enabling more compact models and faster learning. |
Cross-Pair Text Representations for Answer Sentence Selection (D18-1)
Copied to clipboard
| Challenge: | Existing approaches to textual entailment and question answering focus on intra-pair similarity . a simple lexical matching (marked with italics) is not enough to learn a model based on intrapair Qto-A similarities. |
| Approach: | They propose to compute scalar products representing similarity between members of different pairs instead of using a single vector for each pair. |
| Outcome: | The proposed approach outperforms more complex models based on neural networks. |
A Semantics-aware Transformer Model of Relation Linking for Knowledge Base Question Answering (2021.acl-short)
Copied to clipboard
Tahira Naseem, Srinivas Ravishankar, Nandana Mihindukulasooriya, Ibrahim Abdelaziz, Young-Suk Lee, Pavan Kapanipathi, Salim Roukos, Alfio Gliozzo, Alexander Gray
| Challenge: | Existing knowledge base question answering systems do not leverage the explicit semantic parse of the question text. |
| Approach: | They propose a transformer-based neural model that leverages the AMR semantic parse of a sentence. |
| Outcome: | The proposed model outperforms the state-of-the-art on 4 popular benchmark datasets. |
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks (N18-2)
Copied to clipboard
| Challenge: | Semantic representations have long been argued as potentially useful for enforcing meaning preservation and improving generalization performance of machine translation methods. |
| Approach: | They propose to integrate semantic representations into neural machine translation by injecting a semantic bias into sentence encoders and achieving improvements in BLEU scores. |
| Outcome: | The proposed representations achieve better BLEU scores over the linguistic-agnostic and syntax-aware versions on the English–German language pair. |
Convolutional Interaction Network for Natural Language Inference (D18-1)
Copied to clipboard
| Challenge: | Attention-based neural models have achieved great success in natural language inference (NLI). |
| Approach: | They propose a general model to capture the interaction between two sentences, which can be an alternative to the attention mechanism for NLI. |
| Outcome: | The proposed model can capture complex interactions on three large datasets. |
Simple and Effective Text Matching with Richer Alignment Features (P19-1)
Copied to clipboard
| Challenge: | Existing models only use a single inter-sequence alignment layer to make full use of this process. |
| Approach: | They propose to keep three key features available for inter-sequence alignment . they conduct experiments on four well-studied benchmark datasets . |
| Outcome: | The proposed model is able to perform on four well-studied datasets with fewer parameters and the inference speed is at least 6 times faster than similar models. |
Coarse-to-Fine Decoding for Neural Semantic Parsing (P18-1)
Copied to clipboard
| Challenge: | Experimental results show that semantic parsing is more efficient than using simple decoders. |
| Approach: | They propose a structure-aware neural architecture which decomposes the semantic parsing process into two stages. |
| Outcome: | The proposed architecture consistently improves performance on four datasets characteristic of different domains and meaning representations. |
Relating Simple Sentence Representations in Deep Neural Networks and the Brain (P19-1)
Copied to clipboard
| Challenge: | Existing deep learning models for natural language processing are not fully studied. |
| Approach: | They investigate whether deep recurrent models learn sentences against those encoded by the brain and whether there is any correspondence between hidden layers of these models and brain regions when processing sentences. |
| Outcome: | The proposed models can be used to synthesize brain data and improve subsequent stimuli decoding accuracy. |
Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering (C18-1)
Copied to clipboard
| Challenge: | Sentence pair modeling is a fundamental technique underlying many NLP tasks. |
| Approach: | They analyze several neural network designs for sentence pair modeling and compare their performance extensively across eight datasets. |
| Outcome: | The proposed models perform well across eight datasets including paraphrase identification, semantic textual similarity, natural language inference, and question answering tasks. |
Bridging the Gap between Relevance Matching and Semantic Matching for Short Text Similarity Modeling (D19-1)
Copied to clipboard
| Challenge: | Existing techniques for relevance and semantic matching cannot be easily adapted to the other. |
| Approach: | They propose a model that incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness. |
| Outcome: | The proposed model incorporates a hybrid encoder module, a relevance matching module and co-attention mechanisms that capture context-aware semantic relatedness. |