Miracle-dz/UniFAR-FTUs huggingface dataset:
Writing Style Author Embedding Evaluation

Learning authors representations from their textual productions is now widely used to solve multiple downstream tasks, such as clas-siﬁcation, link prediction or user recommendation. Author embedding methods are often built on top of either Doc2Vec (Le and Mikolov, 2014) or the Transformer architecture (Devlin et al., 2019). Evaluating the quality of these embeddings and what they capture is a difﬁcult task. Most articles use either clas-siﬁcation accuracy or authorship attribution, which does not clearly measure the quality of the representation space, if it really captures what it has been built for. In this paper, we pro-pose a novel evaluation framework of author embedding methods based on the writing style. It allows to quantify if the embedding space effectively captures a set of stylistic features, chosen to be the best proxy of an author writing style. This approach gives less importance to the topics conveyed by the documents. It turns out that recent models are mostly driven by the inner semantic of authors’ production. They are outperformed by simple baselines, based on state-of-the-art pretrained sentence embedding models, on several linguistic axes. These baselines can grasp complex linguistic phenomena and writing style more efﬁciently, paving the way for designing new style-driven author embedding models.
Supervised Identification of Participant Slots in Contracts

This paper presents a technique for the identification of participant slots in English language contracts. Taking inspiration from unsupervised slot extraction techniques, the system presented here uses a supervised approach to identify terms used to refer to a genre-specific slot in novel contracts. We evaluate the system in multiple feature configurations to demonstrate that the best performing system in both genres of contracts omits the exact mention form from consideration—even though such mention forms are often the name of the slot under consideration—and is instead based solely on the dependency label and parent; in other words, a more reliable quantification of a party’s role in a contract is found in what they do rather than what they are named.
Controlled tasks for model analysis: Retrieving discrete information from sequences

In recent years, the NLP community has shown increasing interest in analysing how deep learning models work. Given that large models trained on complex tasks are difficult to inspect, some of this work has focused on controlled tasks that emulate specific aspects of language. We propose a new set of such controlled tasks to explore a crucial aspect of natural language processing that has not received enough attention: the need to retrieve discrete information from sequences. We also study model behavior on the tasks with simple instantiations of Transformers and LSTMs. Our results highlight the beneficial role of decoder attention and its sometimes unexpected interaction with other components. Moreover, we show that, for most of the tasks, these simple models still show significant difficulties. We hope that the community will take up the analysis possibilities that our tasks afford, and that a clearer understanding of model behavior on the tasks will lead to better and more transparent models.
Expanding horizons for commercial vermiculture : Earthworms and composting

Technologies based on eathworms offer effective methods to compost larger portions of organics in the residential and commercial waste streams. «I've never seen any mechanical technology do a better job than the eathworms themselves». California's draft regulations exclude vermicomposting operations from the notification and permitting that would be required of most larger facilities using thermophilic composting. «Worms derive their nutrients from the microbes and fungi that grow on organic matter, not the organic matter itself. As they ingest material, it is reduced in size and what comes out the other end is much more microbiologically active than what went in». In California interst in vermiculture has escalated over the past two years. One operator added a demonstration area for school tours
Multi-lingual Discourse Segmentation and Connective Identification: MELODI at Disrpt2021

We present an approach for discourse segmentation and discourse connective identification, both at the sentence and document level, within the Disrpt 2021 shared task, a multi-lingual and multi-formalism evaluation campaign. Building on the most successful architecture from the 2019 similar shared task, we leverage datasets in the same or similar languages to augment training data and improve on the best systems from the previous campaign on 3 out of 4 subtasks, with a mean improvement on all 16 datasets of 0.85%. Within the Disrpt 21 campaign the system ranks 3rd overall, very close to the 2nd system, but with a significant gap with respect to the best system, which uses a rich set of additional features. The system is nonetheless the best on languages that benefited from crosslingual training on sentence internal segmentation (German and Spanish).
Counterfactual Matters: Intrinsic Probing For Dialogue State Tracking

A Dialogue State Tracker (DST) is a core component of modular task-oriented dialogue systems. Tremendous research progress has been made in past ten years to improve performance of DSTs especially on benchmark datasets. However, their generalization to novel and realistic scenarios beyond the held-out conversations is limited. In this paper, we design experimental studies to answer: 1) How does the distribution of dialogue data affect the performance of DSTs? 2) What are effective ways to probe counterfactual matter for DSTs? Our findings are: the performance variance of generative DSTs is not only due to the model structure itself, but can be attributed to the distribution of cross-domain values. Evaluating iconic generative DST models on MultiWOZ dataset with counterfactuals results in a significant performance drop of up to 34.64% (from 50.91% to 16.27%) in absolute joint goal accuracy. It is believed that our experimental results can guide the future work to better understand the intrinsic core of DST and rethink the suitable way for specific tasks given the application property.
ParsTwiNER: A Corpus for Named Entity Recognition at Informal Persian

As a result of unstructured sentences and some misspellings and errors, finding named entities in a noisy environment such as social media takes much more effort. ParsTwiNER contains about 250k tokens, based on standard instructions like MUC-6 or CoNLL 2003, gathered from Persian Twitter. Using Cohen’s Kappa coefficient, the consistency of annotators is 0.95, a high score. In this study, we demonstrate that some state-of-the-art models degrade on these corpora, and trained a new model using parallel transfer learning based on the BERT architecture. Experimental results show that the model works well in informal Persian as well as in formal Persian.
Semi-automatic Triage of Requests for Free Legal Assistance

Free legal assistance is critically under-resourced, and many of those who seek legal help have their needs unmet. A major bottleneck in the provision of free legal assistance to those most in need is the determination of the precise nature of the legal problem. This paper describes a collaboration with a major provider of free legal assistance, and the deployment of natural language processing models to assign area-of-law categories to real-world requests for legal assistance. In particular, we focus on an investigation of models to generate efficiencies in the triage process, but also the risks associated with naive use of model predictions, including fairness across different user demographics.
Contrapositive Local Class Inference

Certain types of classification problems may be performed at multiple levels of granularity; for example, we might want to know the sentiment polarity of a document or a sentence, or a phrase. Often, the prediction at a greater-context (e.g., sentences or paragraphs) may be informative for a more localized prediction at a smaller semantic unit (e.g., words or phrases). However, directly inferring the most salient local features from the global prediction may overlook the semantics of this relationship. This work argues that inference along the contraposition relationship of the local prediction and the corresponding global prediction makes an inference framework that is more accurate and robust to noise. We show how this contraposition framework can be implemented as a transfer function that rewrites a greater-context from one class to another and demonstrate how an appropriate transfer function can be trained from a noisy user-generated corpus. The experimental results validate our insight that the proposed contrapositive framework outperforms the alternative approaches on resource-constrained problem domains.
Theoretical and Practical Issues in the Semantic Annotation of Four Indigenous Languages

Computational resources such as semantically annotated corpora can play an important role in enabling speakers of indigenous minority languages to participate in government, education, and other domains of public life in their own language. However, many languages – mainly those with small native speaker populations and without written traditions – have little to no digital support. One hurdle in creating such resources is that for many languages, few speakers would be capable of annotating texts – a task which requires literacy and some linguistic training – and that these experts’ time is typically in high demand for language planning work. This paper assesses whether typologically trained non-speakers of an indigenous language can feasibly perform semantic annotation using Uniform Meaning Representations, thus allowing for the creation of computational materials without putting further strain on community resources.
Event and Entity Coreference using Trees to Encode Uncertainty in Joint Decisions

Coreference decisions among event mentions and among co-occurring entity mentions are highly interdependent, thus motivating joint inference. Capturing the uncertainty over each variable can be crucial for inference among multiple dependent variables. Previous work on joint coreference employs heuristic approaches, lacking well-defined objectives, and lacking modeling of uncertainty on each side of the joint problem. We present a new approach of joint coreference, including (1) a formal cost function inspired by Dasgupta’s cost for hierarchical clustering, and (2) a representation for uncertainty of clustering of event and entity mentions, again based on a hierarchical structure. We describe an alternating optimization method for inference that when clustering event mentions, considers the uncertainty of the clustering of entity mentions and vice-versa. We show that our proposed joint model provides empirical advantages over state-of-the-art independent and joint models.
Intrinsic evaluation of language models for code-switching

Language models used in speech recognition are often either evaluated intrinsically using perplexity on test data, or extrinsically with an automatic speech recognition (ASR) system. The former evaluation does not always correlate well with ASR performance, while the latter could be specific to particular ASR systems. Recent work proposed to evaluate language models by using them to classify ground truth sentences among alternative phonetically similar sentences generated by a fine state transducer. Underlying such an evaluation is the assumption that the generated sentences are linguistically incorrect. In this paper, we first put this assumption into question, and observe that alternatively generated sentences could often be linguistically correct when they differ from the ground truth by only one edit. Secondly, we showed that by using multi-lingual BERT, we can achieve better performance than previous work on two code-switching data sets. Our implementation is publicly available on Github at https://github.com/sikfeng/language-modelling-for-code-switching.
From Stock Prediction to Financial Relevance: Repurposing Attention Weights to Assess News Relevance Without Manual Annotations

We present a method to automatically identify financially relevant news using stock price movements and news headlines as input. The method repurposes the attention weights of a neural network initially trained to predict stock prices to assign a relevance score to each headline, eliminating the need for manually labeled training data. Our experiments on the four most relevant US stock indices and 1.5M news headlines show that the method ranks relevant news highly, positively correlated with the accuracy of the initial stock price prediction task.
A Text Editing Approach to Joint Japanese Word Segmentation, POS Tagging, and Lexical Normalization

Lexical normalization, in addition to word segmentation and part-of-speech tagging, is a fundamental task for Japanese user-generated text processing. In this paper, we propose a text editing model to solve the three task jointly and methods of pseudo-labeled data generation to overcome the problem of data deficiency. Our experiments showed that the proposed model achieved better normalization performance when trained on more diverse pseudo-labeled data.
Named Entity Recognition in Historic Legal Text: A Transformer and State Machine Ensemble Method

Older legal texts are often scanned and digitized via Optical Character Recognition (OCR), which results in numerous errors. Although spelling and grammar checkers can correct much of the scanned text automatically, Named Entity Recognition (NER) is challenging, making correction of names difficult. To solve this, we developed an ensemble language model using a transformer neural network architecture combined with a finite state machine to extract names from English-language legal text. We use the US-based English language Harvard Caselaw Access Project for training and testing. Then, the extracted names are subjected to heuristic textual analysis to identify errors, make corrections, and quantify the extent of problems. With this system, we are able to extract most names, automatically correct numerous errors and identify potential mistakes that can later be reviewed for manual correction.
Controlling Prosody in End-to-End TTS: A Case Study on Contrastive Focus Generation

While End-2-End Text-to-Speech (TTS) has made significant progresses over the past few years, these systems still lack intuitive user controls over prosody. For instance, generating speech with fine-grained prosody control (prosodic prominence, contextually appropriate emotions) is still an open challenge. In this paper, we investigate whether we can control prosody directly from the input text, in order to code information related to contrastive focus which emphasizes a specific word that is contrary to the presuppositions of the interlocutor. We build and share a specific dataset for this purpose and show that it allows to train a TTS system were this fine-grained prosodic feature can be correctly conveyed using control tokens. Our evaluation compares synthetic and natural utterances and shows that prosodic patterns of contrastive focus (variations of Fo, Intensity and Duration) can be learnt accurately. Such a milestone is important to allow, for example, smart speakers to be programmatically controlled in terms of output prosody.
Are We Summarizing the Right Way? A Survey of Dialogue Summarization Data Sets

Dialogue summarization is a long-standing task in the field of NLP, and several data sets with dialogues and associated human-written summaries of different styles exist. However, it is unclear for which type of dialogue which type of summary is most appropriate. For this reason, we apply a linguistic model of dialogue types to derive matching summary items and NLP tasks. This allows us to map existing dialogue summarization data sets into this model and identify gaps and potential directions for future work. As part of this process, we also provide an extensive overview of existing dialogue summarization data sets.
The diffusion of scientific terms – tracing individuals’ influence in the history of science for English

Tracing the influence of individuals or groups in social networks is an increasingly popular task in sociolinguistic studies. While methods to determine someone’s influence in shortterm contexts (e.g., social media, on-line political debates) are widespread, influence in longterm contexts is less investigated and may be harder to capture. We study the diffusion of scientific terms in an English diachronic scientific corpus, applying Hawkes Processes to capture the role of individual scientists as “influencers” or “influencees” in the diffusion of new concepts. Our findings on two major scientific discoveries in chemistry and astronomy of the 18th century reveal that modelling both the introduction and diffusion of scientific terms in a historical corpus as Hawkes Processes allows detecting patterns of influence between authors on a long-term scale.
To What Extent Can English-as-a-Second Language Learners Read Economic News Texts?

In decision making in the economic field, an especially important requirement is to rapidly understand news to absorb ever-changing economic situations. Given that most economic news is written in English, the ability to read such information without waiting for a translation is particularly valuable in economics in contrast to other fields. In consideration of this issue, this research investigated the extent to which non-native English speakers are able to read economic news to make decisions accordingly – an issue that has been rarely addressed in previous studies. Using an existing standard dataset as training data, we created a classifier that automatically evaluates the readability of text with high accuracy for English learners. Our assessment of the readability of an economic news corpus revealed that most news texts can be read by intermediate English learners. We also found that in some cases, readability varies considerably depending on the knowledge of certain words specific to the economic field.
A Multilingual Approach to Identify and Classify Exceptional Measures against COVID-19

The COVID-19 pandemic has witnessed the implementations of exceptional measures by governments across the world to counteract its impact. This work presents the initial results of an on-going project, EXCEPTIUS, aiming to automatically identify, classify and com- pare exceptional measures against COVID-19 across 32 countries in Europe. To this goal, we created a corpus of legal documents with sentence-level annotations of eight different classes of exceptional measures that are im- plemented across these countries. We evalu- ated multiple multi-label classifiers on a manu- ally annotated corpus at sentence level. The XLM-RoBERTa model achieves highest per- formance on this multilingual multi-label clas- sification task, with a macro-average F1 score of 59.8%.
Supporting users with credibility assessments of health Tweets

Twitter is one of the most popular social media platforms nowadays with more than 500 million tweets being send on a daily basis. A great variety of topics is discussed on Twitter and health has become a widely mentioned topic on Twitter. Users share updates regarding their own personal health situation and they use Twitter to look up health information. Inexperienced users have difficulty assessing the credibility of information contained in tweets. Additional visual and textual cues can be added to the tweets that enhance a users ability to determine the credibility. We define different types of feature sets to represent each tweet, which are then extracted and these features are used for automatic methods that can determine the credibility of health tweets. We narrow our domain to tweets about cancer and for each tweet we assess whether it’s credible, neutral or non-credible. We also investigate the different types of cancer tweets and how the credibility differs between the categories of tweets. We evaluate our research by using a labeled ground truth obtained via the crowd-sourcing platform CrowdFlower. Our results show that automatically supporting users with credibility assessments of health tweets is feasible and can be employed in practice but still leaves room for improvement.
The acquisition of boron tolerance by salt pretreatment in two sunflower cultivars

: This study was carried out to investigate the ameliorative effects of salt pretreatment against B toxicity in two sunflower cultivars ( Helianthus annuus L. ‘Sanbro’ and ‘Tarsan-1018’) differing in salt tolerance. Seedlings were grown in perlite with modified half-strength Hoagland’s solution for 15 days and then they were divided into two groups: salt-pretreated (75 mM NaCl for 5 days) and B-treated (control, 2, 4, and 8 mM B for 10 days). In both cultivars, the biomass of root and shoot decreased depending on B accumulation, especially at 8 mM. The translocation factor values indicated that B uptaken by roots of the genotypes were translocated to the leaves. High B accumulation adversely affected the water balance and membrane integrity of the leaves. Additionally, toxic B levels caused changes in the some JIP tests and slow fluorescence parameters (ABS/RC, TR 0 /RC, ET 0 /RC, RE 0 /RC, DI 0 /RC, Area, φ E0 , φ R0 and j D0 , ФPSII, ETR) of both cultivars and these changes led to a significant decrease in photosynthetic performance (PI ABS and PI TOTAL ). Salt pretreatment ameliorated the damaging effects of toxic B on membrane integrity, water content, and the photosynthetic process; decreased B accumulation; and improved the membrane stability. Both cultivars acquired tolerance against B toxicity with salt pretreatment and survived in increasing boron toxicity. We conclude that sunflower can be used for phytoremediation purposes for boron-contaminated soils. Additionally, this study is the first report to reveal that moderate salt stress pretreatment alleviates B toxicity and provides tolerance to B. This alleviation might be achieved by NaCl to decrease the boron uptake from the roots.
Methodology for interpretation of fissile mass flow measurements

This paper describes a non-intrusive measurement technique to monitor the mass flow rate of fissile material in gaseous or liquid streams. This fissile mass flow monitoring system determines the fissile mass flow rate by relying on two independent measurements: (1) a time delay along a given length of pipe, which is inversely proportional to the fissile material flow velocity, and (2) an amplitude measurement, which is proportional to the fissile concentration (e.g., grams of {sup 235}U per length of pipe). The development of this flow monitor was first funded by DOE/NE in September 95, and initial experimental demonstration by ORNL was described in the 37th INMM meeting held in July 1996. This methodology was chosen by DOE/NE for implementation in November 1996; it has been implemented in hardware/software and is ready for installation. This paper describes the methodology used to interpret the data measured by the fissile mass flow monitoring system and the models used to simulate the transport of fission fragments from the source location to the detectors.