Large, pre-trained language models (PLMs) such as BERT and GPT have drastically changed the Natural Language Processing (NLP) field. For numerous NLP tasks, approaches leveraging PLMs have achieved state-of-the-art performance. The key idea is to learn a generic, latent representation of language from a generic task once, then share it across disparate NLP tasks. Language modeling serves as the generic task, one with abundant self-supervised text available for extensive training. This article presents the key fundamental concepts of PLM architectures and a comprehensive view of the shift to PLM-driven NLP techniques. It surveys work applying the pre-training then fine-tuning, prompting, and text generation approaches. In addition, it discusses PLM limitations and suggested directions for future research. 

https://dl.acm.org/doi/pdf/10.1145/3605943

Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey

Prompt tuning, which only tunes continuous prompts with a frozen language model, substantially reduces per-task storage and memory usage at training. However, in the context of NLU, prior work reveals that prompt tuning does not perform well for normal-sized pre-trained models. We also find that existing methods of prompt tuning cannot handle hard sequence tagging tasks, indicating a lack of universality. We present a novel empirical finding that properly optimized prompt tuning can be universally effective across a wide range of model scales and NLU tasks. It matches the performance of fine-tuning while having only 0.1\%-3\% tuned parameters. Our method P-Tuning v2 is not a new method, but a version of prefix-tuning \cite{li2021prefix} optimized and adapted for NLU. Given the universality and simplicity of P-Tuning v2, we believe it can serve as an alternative to fine-tuning and a strong baseline for future research.

P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks

We show that it is feasible to perform entity linking by training a dual encoder (two-tower) model that encodes mentions and entities in the same dense vector space, where candidate entities are retrieved by approximate nearest neighbor search. Unlike prior work, this setup does not rely on an alias table followed by a re-ranker, and is thus the first fully learned entity retrieval model. We show that our dual encoder, trained using only anchor-text links in Wikipedia, outperforms discrete alias table and BM25 baselines, and is competitive with the best comparable results on the standard TACKBP-2010 dataset. In addition, it can retrieve candidates extremely fast, and generalizes well to a new dataset derived from Wikinews. On the modeling side, we demonstrate the dramatic value of an unsupervised negative mining algorithm for this task.

/pdf/learning-dense-representations-for-entity-retrieval-26ewtxdptf.pdf

Learning Dense Representations for Entity Retrieval

In this paper we overview the 2014 Entity Recognition and Disambiguation Challenge (ERD'14), which took place from March to June 2014 and was summarized in a dedicated workshop at SIGIR 2014. The main goal of the ERD challenge was to promote research in recognition and disambiguation of entities in unstructured text. Unlike many past entity linking challenges, no mention segmentations were given to the participating systems for a given document. Participants were asked to implement a web service for their system to minimize human involvement during evaluation and to enable measuring the processing times. The challenge has attracted a lot of interest (over 100 teams registered, and 27 of those submitted final results).In this paper we cover the task definition, issues encountered during annotation, and provide a detailed analysis of all the participating systems. Specifically, we show how we adapted the pooling technique to address the difficulties of gathering annotations for the entity linking task. We also summarize the ERD workshop that followed the challenge, including the oral and poster presentations as well as the invited talks.

ERD'14: entity recognition and disambiguation challenge

Abstract Deep learning models can accurately predict molecular properties and help making the search for potential drug candidates faster and more efficient. Many existing methods are purely data driven, focusing on exploiting the intrinsic topology and construction rules of molecules without any chemical prior information. The high data dependency makes them difficult to generalize to a wider chemical space and leads to a lack of interpretability of predictions. Here, to address this issue, we introduce a chemical element-oriented knowledge graph to summarize the basic knowledge of elements and their closely related functional groups. We further propose a method for knowledge graph-enhanced molecular contrastive learning with functional prompt (KANO), exploiting external fundamental domain knowledge in both pre-training and fine-tuning. Specifically, with element-oriented knowledge graph as a prior, we first design an element-guided graph augmentation in contrastive-based pre-training to explore microscopic atomic associations without violating molecular semantics. Then, we learn functional prompts in fine-tuning to evoke the downstream task-related knowledge acquired by the pre-trained model. Extensive experiments show that KANO outperforms state-of-the-art baselines on 14 molecular property prediction datasets and provides chemically sound explanations for its predictions. This work contributes to more efficient drug design by offering a high-quality knowledge prior, interpretable molecular representation and superior prediction performance. 

/pdf/knowledge-graph-enhanced-molecular-contrastive-learning-with-7n1kq6ev.pdf

Knowledge graph-enhanced molecular contrastive learning with functional prompt

Relation extraction systems require large amounts of labeled examples which are costly to annotate. In this work we reformulate relation extraction as an entailment task, with simple, hand-made, verbalizations of relations produced in less than 15 min per relation. The system relies on a pretrained textual entailment engine which is run as-is (no training examples, zero-shot) or further fine-tuned on labeled examples (few-shot or fully trained). In our experiments on TACRED we attain 63% F1 zero-shot, 69% with 16 examples per relation (17% points better than the best supervised system on the same conditions), and only 4 points short to the state-of-the-art (which uses 20 times more training data). We also show that the performance can be improved significantly with larger entailment models, up to 12 points in zero-shot, allowing to report the best results to date on TACRED when fully trained. The analysis shows that our few-shot systems are specially effective when discriminating between relations, and that the performance difference in low data regimes comes mainly from identifying no-relation cases.

Label Verbalization and Entailment for Effective Zero- and Few-Shot Relation Extraction

Hyperlinks and other relations in Wikipedia are a extraordinary resource which is still not fully understood. In this paper we study the different types of links in Wikipedia, and contrast the use of the full graph with respect to just direct links. We apply a well-known random walk algorithm on two tasks, word relatedness and named-entity disambiguation. We show that using the full graph is more effective than just direct links by a large margin, that non-reciprocal links harm performance, and that there is no benefit from categories and infoboxes, with coherent results on both tasks. We set new state-of-the-art figures for systems based on Wikipedia links, comparable to systems exploiting several information sources and/or supervised machine learning. Our approach is open source, with instruction to reproduce results, and amenable to be integrated with complementary text-based methods.

pdf/studying-the-wikipedia-hyperlink-graph-for-relatedness-and-364s40wf5q.pdf

Studying the Wikipedia Hyperlink Graph for Relatedness and Disambiguation.

Digitised Cultural Heritage (CH) items usually have short descriptions and lack rich contextual information. Wikipedia articles, on the contrary, include in-depth descriptions and links to related articles, which motivate the enrichment of CH items with information from Wikipedia. In this paper we explore the feasibility of finding matching articles in Wikipedia for a given Cultural Heritage item. We manually annotated a random sample of items from Europeana, and performed a qualitative and quantitative study of the issues and problems that arise, showing that each kind of CH item is different and needs a nuanced definition of what ``matching article'' means. In addition, we test a well-known wikification (aka entity linking) algorithm on the task. Our results indicate that a substantial number of items can be effectively linked to their corresponding Wikipedia article.

/pdf/matching-cultural-heritage-items-to-wikipedia-4qsb8dan24.pdf

Matching Cultural Heritage items to Wikipedia

Named entity disambiguation is the task of linking entity mentions to their intended referent, as represented in a Knowledge Base, usually derived from Wikipedia. In this paper, we combine local mention context and global hyperlink structure from Wikipedia in a probabilistic framework. We test our method in eight datasets, improving the state-of-the-art results in five. Our results show that the two models of context, namely, words in the context and hyperlink pathways to other entities in the context, are complementary. Our results are not tuned to any of the datasets, showing that it is robust to out-of-domain scenarios, and that further improvements are possible.

/pdf/combining-mention-context-and-hyperlinks-from-wikipedia-for-545kavah6t.pdf

Combining Mention Context and Hyperlinks from Wikipedia for Named Entity Disambiguation

Increased top-down semantic processing in natural speech linked to better reading in dyslexia

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