Neural Natural Language Inference Models Enhanced with External Knowledge

By Qian Chen, Xiao-Dan Zhu, Zhenhua Ling, Diana Inkpen, Si Wei. 2017

227 citations recorded by the source corpus.

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Natural Language Processing

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Citations in the corpus

Knowledge Enhanced Contextual Word Representations (2019, background): “Task-specific KB architectures Other work has focused on integrating KBs into neural architectures for specific downstream tasks (Yang and Mitchell, 2017; Sun et al., 2018; Chen et al., 2018; Bauer et al., 2018; Mihaylov and Frank, 2018; Wang and Jiang, 2019; Yang et al., 2019).”
Breaking NLI Systems with Sentences that Require Simple Lexical Inferences (2018, methodology): “E SIM (Enhanced Sequential Inference Model, Chen et al., 2017) is a hybrid TreeLSTM-based and biLSTM-based model.”
ERNIE: Enhanced Language Representation with Informative Entities (2019, background): “…signiﬁcantly enhance original models, such as reading comprehension (Mihaylov and Frank, 2018; Zhong et al., 2018), machine translation (Zaremoodi et al., 2018), natural language inference (Chen et al., 2018), knowledge acquisition (Han et al., 2018a), and dialog systems (Madotto et al., 2018).”
Guiding Extractive Summarization with Question-Answering Rewards (2019, methodology): “A soft-max function is used to estimate a probability distribution over the space of candidate answers: Such a fully-connected output layer has achieved success on natural language inference (Mou et al., 2016; Chen et al., 2018); here we test its efﬁcacy on answer selection.”
Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (2019, background): “Weissenborn et al. (2017), Bauer et al. (2018), Mihaylov and Frank (2018), Pan et al. (2019), Chen et al. (2018), Wang et al. (2018) follow a retrieve-then-encode paradigm, i.e., they ﬁrst retrieve relevant knowledge from KBs, and only the retrieved knowledge relevant locally to the reading text…”
Can Neural Networks Understand Monotonicity Reasoning? (2019, background): “In particular, all models underperformed the majority baseline on downward inferences, despite some models having rich lexical knowledge from a knowledge base (KIM) or pretraining (BERT).”
COVIDRead: A Large-scale Question Answering Dataset on COVID-19 (2021, methodology): “With this motivation we determine the entailment relation between machine-generated questions and reference questions by applying one of the state-of-the-art entailment models that is equipped with external knowledge [17].”
Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020, methodology): “Some previous models are developed on the of-ﬁcial baseline (Thorne et al., 2018) with three step pipeline (Chen et al., 2017a) for fact veriﬁcation (Hanselowski et al., 2018; Yoneda et al., 2018; Yin and Roth, 2018; Nie et al., 2019).”
Enhancing Sentence Embedding with Generalized Pooling (2018, methodology): “To help replicate our results, we publish our code3, which is developed from our codebase for multiple tasks (Chen et al., 2018; Chen et al., 2017a; Chen et al., 2016; Zhang et al., 2017).”
On-The-Fly Information Retrieval Augmentation for Language Models (2020, background): “ral language inference (Chen et al., 2018; Yang et al., 2019), language generation (Parthasarathi and Pineau, 2018), knowledge base completion (Toutanova et al.”
Testing the Generalization Power of Neural Network Models across NLI Benchmarks (2018, methodology): “The sys-tem that was more difﬁcult to break and had the less loss in accuracy was the system by Chen et al. (2018) which utilizes external knowledge taken from WordNet (Miller, 1995).”
Reasoning Over Virtual Knowledge Bases With Open Predicate Relations (2021, methodology): “…that use KB triples have been with only a few hundreds of triples in the model at any point, necessitating a separate heuristic process to retrieve candidate KB triples (Ahn et al., 2016; Henaff et al., 2016; Weissenborn et al., 2017; Chen et al., 2018; Mihaylov & Frank, 2018; Logan et al., 2019a).”
Sequential Attention-based Network for Noetic End-to-End Response Selection (2019, background): “ing the efficacy of external knowledge (Chen et al. 2018), such as knowledge graph and user profile.”
Knowledge Fusion and Semantic Knowledge Ranking for Open Domain Question Answering (2020, background): “Other models such as in the work of (Mi-haylov and Frank, 2018; Chen et al., 2018; Yang et al., 2019; Wang and Jiang, 2019) embed syntactic or semantic knowledge into knowledge enriched embeddings.”
Commonsense for Generative Multi-Hop Question Answering Tasks (2018, background): “There have been several attempts at using external knowledge to boost model performance on a variety of tasks: Chen et al. (2018) showed that adding lexical information from semantic databases such as WordNet improves performance on NLI; Xu et al. (2017) used a gated recall-LSTM mechanism to…”
Answering Science Exam Questions Using Query Rewriting with Background Knowledge (2018, background): “A I] 1 5 Se p Roth, and Schütze 2018; Parikh et al. 2016; Chen et al. 2018) and have gained recent popularity due to the release of large datasets (Bowman et al. 2015; Khot, Sabharwal, and Clark 2018; Williams, Nangia, and Bowman 2018).”
How Additional Knowledge can Improve Natural Language Commonsense Question Answering (2019, background): “External Knowledge: Models that integrate ex-ternal knowledge have been introduced in (Mi-haylov and Frank, 2018; Chen et al., 2018; Yang et al., 2019; Wang and Jiang, 2019).”
Deep entity matching with pre-trained language models (2020, background): “External knowledge is known to be effective in improving neural network models in NLP tasks [5, 40].”