Ireland

Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples. In this paper, we present the first pipeline for building Chinese entailment graphs. In this pipeline, we present a novel high-recall open relation extraction (ORE) method and the first Chinese fine-grained entity typing dataset following the FIGER type ontology. Through experiments on the popular Levy-Holt dataset, translated into Chinese, we show that our Chinese entailment graph outperforms a range of strong baselines by large margins. Moreover, an ensemble of Chinese and English entailment graphs sets a new unsupervised SOTA on the original Levy-Holt dataset, surpassing previous SOTA by more than 4 AUC points.

ACL 2022

Cross-lingual Inference with A Chinese Entailment Graph

entailment detection

entailment graphs

fine-grained entity typing

open relation extraction

cross-lingual

chinese

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ACL-IJCNLP 2021

The great event is jointly organized by the Association for Computational Linguistics (ACL) and Asian Federation of Natural Language Processing (AFNLP).

findings / work in progress

# Welcome everyone to ACL 2022!

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The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing (NLP). 

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Distributional methods – especially word embeddings in the last few years– have been largely and successfully used. However distributional methods have a strong limitation: word meaning can be assigned only for words where sufficient contexts can be gathered. Rare words are not covered and become the classical out-of-vocabulary words.
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Commonsense inference poses a unique challenge to reason and generate the physical, social, and causal conditions of a given event. Existing approaches to commonsense inference utilize commonsense transformers, which are large-scale language models that learn commonsense knowledge graphs. However, they suffer from a lack of coverage and expressive diversity of the graphs, resulting in a degradation of the representation quality. In this paper, we focus on addressing missing relations in commonsense knowledge graphs, and propose a novel contrastive learning framework called SOLAR . Our framework contrasts sets of semantically similar and dissimilar events, learning richer inferential knowledge compared to existing approaches. Empirical results demonstrate the efficacy of SOLAR in commonsense inference of diverse commonsense knowledge graphs. Specifically, SOLAR outperforms the state-of-the-art commonsense transformer on commonsense inference with ConceptNet by 1.84% on average among 8 automatic evaluation metrics. In-depth analysis of SOLAR sheds light on the effects of the missing relations utilized in learning commonsense knowledge graphs.

Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense Inference

Traditionally, Latent Dirichlet Allocation (LDA) ingests words in a collection of documents to discover their latent topics using word-document co-occurrences. Previous studies show that representing bigrams collocations in the input can improve topic coherence in English. However, it is unclear how to achieve the best results for languages without marked word boundaries such as Chinese and Thai. Here, we explore the use of retokenization based on chi-squared measures, t-statistics, and raw frequency to merge frequent token ngrams into collocations when preparing input to the LDA model. Based on the goodness of fit and the coherence metric, we show that topics trained with merged tokens result in topic keys that are clearer, more coherent, and more effective at distinguishing topics than those of unmerged models.

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Morphosyntactic Tagging with Pre-trained Language Models for Arabic and its Dialects

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Recent work by Søgaard (2020) showed that, treebank size aside, overlap between train- ing and test graphs (termed leakage) explains more of the observed variation in dependency parsing performance than other explanations. In this work we revisit this claim, testing it on more models and languages. We find that it only holds for zero-shot cross-lingual settings. We then propose a more fine-grained measure of such leakage which, unlike the original mea- sure, not only explains but also correlates with observed performance variation.

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We provide the first exploration of sentence embeddings from text-to-text transformers (T5) including the effects of scaling up sentence encoders to 11B parameters. Sentence embeddings are broadly useful for language processing tasks. While T5 achieves impressive performance on language tasks, it is unclear how to produce sentence embeddings from encoder-decoder models. We investigate three methods to construct SentenceT5 (ST5) models: two utilize only the T5 encoder and one using the full T5 encoderdecoder. We establish a new sentence representation transfer benchmark, SentGLUE, which extends the SentEval toolkit to nine tasks from the GLUE benchmark (Wang et al., 2018). Our encoder-only models outperform the previous best models on both SentEval and SentGLUE transfer tasks, including semantic textual similarity (STS). Scaling up ST5 from millions to billions of parameters shown to consistently improve performance. Finally, our encoderdecoder method achieves a new state-of-theart on STS when using sentence embeddings.

Cross-lingual Inference with A Chinese Entailment Graph

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