ymoslem/acl-abstracts huggingface dataset:
Fine-grained Named Entity Annotations for German Biographic Interviews (2020)

We present a fine-grained NER annotations with 30 labels and apply it to German data. Building on the OntoNotes 5.0 NER inventory, our scheme is adapted for a corpus of transcripts of biographic interviews by adding categories for AGE and LAN(guage) and also features extended numeric and temporal categories. Applying the scheme to the spoken data as well as a collection of teaser tweets from newspaper sites, we can confirm its generality for both domains, also achieving good inter-annotator agreement. We also show empirically how our inventory relates to the well-established 4-category NER inventory by re-annotating a subset of the GermEval 2014 NER coarse-grained dataset with our fine label inventory. Finally, we use a BERT-based system to establish some baseline models for NER tagging on our two new datasets. Global results in in-domain testing are quite high on the two datasets, near what was achieved for the coarse inventory on the CoNLLL2003 data. Cross-domain testing produces much lower results due to the severe domain differences.
A Broad-coverage Corpus for Finnish Named Entity Recognition (2020)

We present a new manually annotated corpus for broad-coverage named entity recognition for Finnish. Building on the original Universal Dependencies Finnish corpus of 754 documents (200,000 tokens) representing ten different genres of text, we introduce annotation marking person, organization, location, product and event names as well as dates. The new annotation identifies in total over 10,000 mentions. An evaluation of inter-annotator agreement indicates that the quality and consistency of annotation are high, at 94.5{\%} F-score for exact match. A comprehensive evaluation using state-of-the-art machine learning methods demonstrates that the new resource maintains compatibility with a previously released single-domain corpus for Finnish NER and makes it possible to recognize named entity mentions in texts drawn from most domains at precision and recall approaching or exceeding 90{\%}. Remaining challenges such as the identification of names in blog posts and transcribed speech are also identified. The newly introduced Turku NER corpus and related resources introduced in this work are released under open licenses via \url{https://turkunlp.org/turku-ner-corpus} .
Embeddings for Named Entity Recognition in Geoscience Portuguese Literature (2020)

This work focuses on Portuguese Named Entity Recognition (NER) in the Geology domain. The only domain-specific dataset in the Portuguese language annotated for NER is the GeoCorpus. Our approach relies on BiLSTM-CRF neural networks (a widely used type of network for this area of research) that use vector and tensor embedding representations. Three types of embedding models were used (Word Embeddings, Flair Embeddings, and Stacked Embeddings) under two versions (domain-specific and generalized). The domain specific Flair Embeddings model was originally trained with a generalized context in mind, but was then fine-tuned with domain-specific Oil and Gas corpora, as there simply was not enough domain corpora to properly train such a model. Each of these embeddings was evaluated separately, as well as stacked with another embedding. Finally, we achieved state-of-the-art results for this domain with one of our embeddings, and we performed an error analysis on the language model that achieved the best results. Furthermore, we investigated the effects of domain-specific versus generalized embeddings.
Establishing a New State-of-the-Art for French Named Entity Recognition (2020)

The French TreeBank developed at the University Paris 7 is the main source of morphosyntactic and syntactic annotations for French. However, it does not include explicit information related to named entities, which are among the most useful information for several natural language processing tasks and applications. Moreover, no large-scale French corpus with named entity annotations contain referential information, which complement the type and the span of each mention with an indication of the entity it refers to. We have manually annotated the French TreeBank with such information, after an automatic pre-annotation step. We sketch the underlying annotation guidelines and we provide a few figures about the resulting annotations.
Building OCR/NER Test Collections (2020)

Named entity recognition (NER) identifies spans of text that contain names. Many researchers have reported the results of NER on text created through optical character recognition (OCR) over the past two decades. Unfortunately, the test collections that support this research are annotated with named entities after optical character recognition (OCR) has been run. This means that the collection must be re-annotated if the OCR output changes. Instead by tying annotations to character locations on the page, a collection can be built that supports OCR and NER research without requiring re-annotation when either improves. This means that named entities are annotated on the transcribed text. The transcribed text is all that is needed to evaluate the performance of OCR. For NER evaluation, the tagged OCR output is aligned to the transcriptions the aligned files, creating modified files of each, which are scored. This paper presents a methodology for building such a test collection and releases a collection of Chinese OCR-NER data constructed using the methodology. The paper provides performance baselines for current OCR and NER systems applied to this new collection.
Reconstructing NER Corpora: a Case Study on Bulgarian (2020)

The paper reports on the usage of deep learning methods for improving a Named Entity Recognition (NER) training corpus and for predicting and annotating new types in a test corpus. We show how the annotations in a type-based corpus of named entities (NE) were populated as occurrences within it, thus ensuring density of the training information. A deep learning model was adopted for discovering inconsistencies in the initial annotation and for learning new NE types. The evaluation results get improved after data curation, randomization and deduplication.
MucLex: A German Lexicon for Surface Realisation (2020)

Language resources for languages other than English are often scarce. Rule-based surface realisers need elaborate lexica in order to be able to generate correct language, especially in languages like German, which include many irregular word forms. In this paper, we present MucLex, a German lexicon for the Natural Language Generation task of surface realisation, based on the crowd-sourced online lexicon Wiktionary. MucLex contains more than 100,000 lemmata and more than 670,000 different word forms in a well-structured XML file and is available under the Creative Commons BY-SA 3.0 license.
Generating Major Types of Chinese Classical Poetry in a Uniformed Framework (2020)

Poetry generation is an interesting research topic in the field of text generation. As one of the most valuable literary and cultural heritages of China, Chinese classical poetry is very familiar and loved by Chinese people from generation to generation. It has many particular characteristics in its language structure, ranging from form, sound to meaning, thus is regarded as an ideal testing task for text generation. In this paper, we propose a GPT-2 based uniformed framework for generating major types of Chinese classical poems. We define a unified format for formulating all types of training samples by integrating detailed form information, then present a simple form- stressed weighting method in GPT-2 to strengthen the control to the form of the generated poems, with special emphasis on those forms with longer body length. Preliminary experimental results show this enhanced model can generate Chinese classical poems of major types with high quality in both form and content, validating the effectiveness of the proposed strategy. The model has been incorporated into Jiuge, the most influential Chinese classical poetry generation system developed by Tsinghua University.
Video Caption Dataset for Describing Human Actions in Japanese (2020)

In recent years, automatic video caption generation has attracted considerable attention. This paper focuses on the generation of Japanese captions for describing human actions. While most currently available video caption datasets have been constructed for English, there is no equivalent Japanese dataset. To address this, we constructed a large-scale Japanese video caption dataset consisting of 79,822 videos and 399,233 captions. Each caption in our dataset describes a video in the form of {\textquotedblleft}who does what and where.{\textquotedblright} To describe human actions, it is important to identify the details of a person, place, and action. Indeed, when we describe human actions, we usually mention the scene, person, and action. In our experiments, we evaluated two caption generation methods to obtain benchmark results. Further, we investigated whether those generation methods could specify {\textquotedblleft}who does what and where.{\textquotedblright}
Decode with Template: Content Preserving Sentiment Transfer (2020)

Sentiment transfer aims to change the underlying sentiment of input sentences. The two major challenges in existing works lie in (1) effectively disentangling the original sentiment from input sentences; and (2) preserving the semantic content while transferring the sentiment. We find that identifying the sentiment-irrelevant content from input sentences to facilitate generating output sentences could address the above challenges and then propose the Decode with Template model in this paper. We first mask the explicit sentiment words in input sentences and use the rest parts as templates to eliminate the original sentiment. Then, we input the templates and the target sentiments into our bidirectionally guided variational auto-encoder (VAE) model to generate output. In our method, the template preserves most of the semantics in input sentences, and the bidirectionally guided decoding captures both forward and backward contextual information to generate output. Both two parts contribute to better content preservation. We evaluate our method on two review datasets, Amazon and Yelp, with automatic evaluation methods and human rating. The experimental results show that our method significantly outperforms state-of-the-art models, especially in content preservation.
Best Student Forcing: A Simple Training Mechanism in Adversarial Language Generation (2020)

Language models trained with Maximum Likelihood Estimation (MLE) have been considered as a mainstream solution in Natural Language Generation (NLG) for years. Recently, various approaches with Generative Adversarial Nets (GANs) have also been proposed. While offering exciting new prospects, GANs in NLG by far are nevertheless reportedly suffering from training instability and mode collapse, and therefore outperformed by conventional MLE models. In this work, we propose techniques for improving GANs in NLG, namely Best Student Forcing (BSF), a novel yet simple adversarial training mechanism in which generated sequences of high quality are selected as temporary ground-truth to further train the generator. We also use an ensemble of discriminators to increase training stability and sample diversity. Evaluation shows that the combination of BSF and multiple discriminators consistently performs better than previous GAN approaches over various metrics, and outperforms a baseline MLE in terms of Fr ́ech ́et Distance, a recently proposed metric capturing both sample quality and diversity.
Controllable Sentence Simplification (2020)

Text simplification aims at making a text easier to read and understand by simplifying grammar and structure while keeping the underlying information identical. It is often considered an all-purpose generic task where the same simplification is suitable for all; however multiple audiences can benefit from simplified text in different ways. We adapt a discrete parametrization mechanism that provides explicit control on simplification systems based on Sequence-to-Sequence models. As a result, users can condition the simplifications returned by a model on attributes such as length, amount of paraphrasing, lexical complexity and syntactic complexity. We also show that carefully chosen values of these attributes allow out-of-the-box Sequence-to-Sequence models to outperform their standard counterparts on simplification benchmarks. Our model, which we call ACCESS (as shorthand for AudienCe-CEntric Sentence Simplification), establishes the state of the art at 41.87 SARI on the WikiLarge test set, a +1.42 improvement over the best previously reported score.
Exploring Transformer Text Generation for Medical Dataset Augmentation (2020)

Natural Language Processing (NLP) can help unlock the vast troves of unstructured data in clinical text and thus improve healthcare research. However, a big barrier to developments in this field is data access due to patient confidentiality which prohibits the sharing of this data, resulting in small, fragmented and sequestered openly available datasets. Since NLP model development requires large quantities of data, we aim to help side-step this roadblock by exploring the usage of Natural Language Generation in augmenting datasets such that they can be used for NLP model development on downstream clinically relevant tasks. We propose a methodology guiding the generation with structured patient information in a sequence-to-sequence manner. We experiment with state-of-the-art Transformer models and demonstrate that our augmented dataset is capable of beating our baselines on a downstream classification task. Finally, we also create a user interface and release the scripts to train generation models to stimulate further research in this area.
Multi-lingual Mathematical Word Problem Generation using Long Short Term Memory Networks with Enhanced Input Features (2020)

A Mathematical Word Problem (MWP) differs from a general textual representation due to the fact that it is comprised of numerical quantities and units, in addition to text. Therefore, MWP generation should be carefully handled. When it comes to multi-lingual MWP generation, language specific morphological and syntactic features become additional constraints. Standard template-based MWP generation techniques are incapable of identifying these language specific constraints, particularly in morphologically rich yet low resource languages such as Sinhala and Tamil. This paper presents the use of a Long Short Term Memory (LSTM) network that is capable of generating elementary level MWPs, while satisfying the aforementioned constraints. Our approach feeds a combination of character embeddings, word embeddings, and Part of Speech (POS) tag embeddings to the LSTM, in which attention is provided for numerical values and units. We trained our model for three languages, English, Sinhala and Tamil using separate MWP datasets. Irrespective of the language and the type of the MWP, our model could generate accurate single sentenced and multi sentenced problems. Accuracy reported in terms of average BLEU score for English, Sinhala and Tamil languages were 22.97{\%}, 24.49{\%} and 20.74{\%}, respectively.
Time-Aware Word Embeddings for Three Lebanese News Archives (2020)

Word embeddings have proven to be an effective method for capturing semantic relations among distinct terms within a large corpus. In this paper, we present a set of word embeddings learnt from three large Lebanese news archives, which collectively consist of 609,386 scanned newspaper images and spanning a total of 151 years, ranging from 1933 till 2011. The diversified ideological nature of the news archives alongside the temporal variability of the embeddings offer a rare glimpse onto the variation of word representation across the left-right political spectrum. To train the word embeddings, Google`s Tesseract 4.0 OCR engine was employed to transcribe the scanned news archives, and various archive-level as well as decade-level word embeddings were learnt. To evaluate the accuracy of the learnt word embeddings, a benchmark of analogy tasks was used. Finally, we demonstrate an interactive system that allows the end user to visualize for a given word of interest, the variation of the top-k closest words in the embedding space as a function of time and across news archives using an animated scatter plot.
GGP: Glossary Guided Post-processing for Word Embedding Learning (2020)

Word embedding learning is the task to map each word into a low-dimensional and continuous vector based on a large corpus. To enhance corpus based word embedding models, researchers utilize domain knowledge to learn more distinguishable representations via joint optimization and post-processing based models. However, joint optimization based models require much training time. Existing post-processing models mostly consider semantic knowledge while learned embedding models show less functional information. Glossary is a comprehensive linguistic resource. And in previous works, the glossary is usually used to enhance the word representations via joint optimization based methods. In this paper, we post-process pre-trained word embedding models with incorporating the glossary and capture more topical and functional information. We propose GGP (Glossary Guided Post-processing word embedding) model which consists of a global post-processing function to fine-tune each word vector, and an auto-encoding model to learn sense representations, furthermore, constrains each post-processed word representation and the composition of its sense representations to be similar. We evaluate our model by comparing it with two state-of-the-art models on six word topical/functional similarity datasets, and the results show that it outperforms competitors by an average of 4.1{\%} across all datasets. And our model outperforms GloVe by more than 7{\%}.
High Quality ELMo Embeddings for Seven Less-Resourced Languages (2020)

Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task. We offer precomputed embeddings from popular contextual ELMo model for seven languages: Croatian, Estonian, Finnish, Latvian, Lithuanian, Slovenian, and Swedish. We demonstrate that the quality of embeddings strongly depends on the size of training set and show that existing publicly available ELMo embeddings for listed languages shall be improved. We train new ELMo embeddings on much larger training sets and show their advantage over baseline non-contextual FastText embeddings. In evaluation, we use two benchmarks, the analogy task and the NER task.
Is Language Modeling Enough? Evaluating Effective Embedding Combinations (2020)

Universal embeddings, such as BERT or ELMo, are useful for a broad set of natural language processing tasks like text classification or sentiment analysis. Moreover, specialized embeddings also exist for tasks like topic modeling or named entity disambiguation. We study if we can complement these universal embeddings with specialized embeddings. We conduct an in-depth evaluation of nine well known natural language understanding tasks with SentEval. Also, we extend SentEval with two additional tasks to the medical domain. We present PubMedSection, a novel topic classification dataset focussed on the biomedical domain. Our comprehensive analysis covers 11 tasks and combinations of six embeddings. We report that combined embeddings outperform state of the art universal embeddings without any embedding fine-tuning. We observe that adding topic model based embeddings helps for most tasks and that differing pre-training tasks encode complementary features. Moreover, we present new state of the art results on the MPQA and SUBJ tasks in SentEval.
Language Modeling with a General Second-Order RNN (2020)

Different Recurrent Neural Network (RNN) architectures update their state in different manners as the input sequence is processed. RNNs including a multiplicative interaction between their current state and the current input, second-order ones, show promising performance in language modeling. In this paper, we introduce a second-order RNNs that generalizes existing ones. Evaluating on the Penn Treebank dataset, we analyze how its different components affect its performance in character-lever recurrent language modeling. We perform our experiments controlling the parameter counts of models. We find that removing the first-order terms does not hinder performance. We perform further experiments comparing the effects of the relative size of the state space and the multiplicative interaction space on performance. Our expectation was that a larger states would benefit language models built on longer documents, and larger multiplicative interaction states would benefit ones built on larger input spaces. However, our results suggest that this is not the case and the optimal relative size is the same for both document tokenizations used.
Towards a Gold Standard for Evaluating Danish Word Embeddings (2020)

This paper presents the process of compiling a model-agnostic similarity goal standard for evaluating Danish word embeddings based on human judgments made by 42 native speakers of Danish. Word embeddings resemble semantic similarity solely by distribution (meaning that word vectors do not reflect relatedness as differing from similarity), and we argue that this generalization poses a problem in most intrinsic evaluation scenarios. In order to be able to evaluate on both dimensions, our human-generated dataset is therefore designed to reflect the distinction between relatedness and similarity. The goal standard is applied for evaluating the {\textquotedblleft}goodness{\textquotedblright} of six existing word embedding models for Danish, and it is discussed how a relatively low correlation can be explained by the fact that semantic similarity is substantially more challenging to model than relatedness, and that there seems to be a need for future human judgments to measure similarity in full context and along more than a single spectrum.
Urban Dictionary Embeddings for Slang NLP Applications (2020)

The choice of the corpus on which word embeddings are trained can have a sizable effect on the learned representations, the types of analyses that can be performed with them, and their utility as features for machine learning models. To contribute to the existing sets of pre-trained word embeddings, we introduce and release the first set of word embeddings trained on the content of Urban Dictionary, a crowd-sourced dictionary for slang words and phrases. We show that although these embeddings are trained on fewer total tokens (by at least an order of magnitude compared to most popular pre-trained embeddings), they have high performance across a range of common word embedding evaluations, ranging from semantic similarity to word clustering tasks. Further, for some extrinsic tasks such as sentiment analysis and sarcasm detection where we expect to require some knowledge of colloquial language on social media data, initializing classifiers with the Urban Dictionary Embeddings resulted in improved performance compared to initializing with a range of other well-known, pre-trained embeddings that are order of magnitude larger in size.