Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph

Ji, Haozhe; Pei, Ke; Huang, Shaohan; Wei, Furu; Zhu, Xiaoyan; Huang, Minlie

doi:10.18653/v1/2020.emnlp-main.54

Cited by 68 publications

(74 citation statements)

References 29 publications

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“…Note that GLUCOSE-GPT2 and COINS are using the same knowledge resource, hence the clear performance increase of COINS (+4.92 BLEU score) indicates that jointly learning to generate contextualized inferences rules and missing sentences in a recursive manner can enhance generation quality. 10 (ii) Similar to Ji et al (2020) ). (iii) For COINS, general rules (GR) boost performance more than specific rules, indicating that the sentence generation model generalizes well.…”

Section: Resultsmentioning

confidence: 94%

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COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

Paul

Frank

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In this paper we present COINS, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. We apply COINS to a Narrative Story Completion task that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies. By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent. Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence. We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules. The recursive nature of COINS holds the potential for controlled generation of longer sequences.

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Section: Resultsmentioning

confidence: 94%

“…Automatic Metrics. For Story Ending Generation (SEG) we follow the metrics used in Guan et al (2019); Ji et al (2020): they use BLEU-1/2 to measure n-gram overlap between generated and human-written story endings, and Distinct-n (Li et al, 2016) to measure the generation diversity using maximum mutual information.…”

Section: A3 Story Ending Generation Taskmentioning

confidence: 99%

COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

Paul

Frank

2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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“…We acquire structured knowledge and rationale definitions from ConceptNet 5 and dictionary source 6 separately. For ConceptNet, we extract knowledge with Breadth-First-Search (BFS) algorithm as described in (Ji et al, 2020). For dictionary, we extract the definition of rationales by following (Chen et al, 2020a).…”

Section: Contrastive Explanation Generationmentioning

confidence: 99%

KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

Chen¹,

Ji²,

Zeng³

et al. 2021

Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Confer

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In order to better understand the reason behind model behaviors (i.e., making predictions), most recent work has exploited generative models to provide complementary explanations. However, existing approaches in natural language processing (NLP) mainly focus on "WHY A" rather than contrastive "WHY A NOT B", which is shown to be able to better distinguish confusing candidates and improve model performance in other research fields. In this paper, we focus on generating Contrastive Explanations with counterfactual examples in NLI and propose a novel Knowledge-Aware generation framework (KACE). Specifically, we first identify rationales (i.e., key phrases) from input sentences, and use them as key perturbations for generating counterfactual examples. After obtaining qualified counterfactual examples, we take them along with original examples and external knowledge as input, and employ a knowledge-aware generative pre-trained language model to generate contrastive explanations. Experimental results show that contrastive explanations are beneficial to clarify the difference between predicted answer and other answer options. Moreover, we train an BERT-large based NLI model enhanced with contrastive explanations and achieve an accuracy of 91.9% on SNLI, gaining an improvement of 5.7% against ETPA ("Explain-Then-Predict-Attention") and 0.6% against NILE ("WHY A"). * Work is done during internship at Alibaba Group. † The work is mainly conducted while being at Alibaba Group.

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“…Pre-trained LMs enhanced with commonsense knowledge have also been the models of choice for other text generation tasks, e.g. dialogue generation (Zhou et al, 2018), story ending generation (Guan et al, 2020), or abductive NLI (Ji et al, 2020b). While these models aim at generating explanations for a single statement, or completing a given sequence of sentences, we investigate how to make use of LMs to generate a sentence that fills in implicit knowledge between two sentences.…”

Section: Related Workmentioning

confidence: 99%

Reconstructing Implicit Knowledge with Language Models

Becker

Liang²,

Frank

2021

Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Ar

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In this work we propose an approach for generating statements that explicate implicit knowledge connecting sentences in text. We make use of pre-trained language models which we refine by fine-tuning them on specifically prepared corpora that we enriched with implicit information, and by constraining them with relevant concepts and connecting commonsense knowledge paths. Manual and automatic evaluation of the generations shows that by refining language models as proposed, we can generate coherent and grammatically sound sentences that explicate implicit knowledge which connects sentence pairs in texts -on both in-domain and out-of-domain test data.

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Language Generation with Multi-Hop Reasoning on Commonsense Knowledge Graph

Cited by 68 publications

References 29 publications

COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

COINS: Dynamically Generating COntextualized Inference Rules for Narrative Story Completion

KACE: Generating Knowledge Aware Contrastive Explanations for Natural Language Inference

Reconstructing Implicit Knowledge with Language Models

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