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Cognate prediction is the task of generating, in a given language, the likely cognates of words in a related language, where cognates are words in related languages that have evolved from a common ancestor word. It is a task for which little data exists and which can aid linguists in the discovery of previously undiscovered relations. Previous work has applied machine translation (MT) techniques to this task, based on the tasks&#39; similarities, without, however, studying their numerous differences or optimising architectural choices and hyper-parameters. In this paper, we investigate whether cognate prediction can benefit from insights from low-resource MT. We first compare statistical MT (SMT) and neural MT (NMT) architectures in a bilingual setup. We then study the impact of employing data augmentation techniques commonly seen to give gains in low-resource MT: monolingual pretraining, backtranslation and multilinguality.
Our experiments on several Romance languages show that cognate prediction behaves only to a certain extent like a standard low-resource MT task. In particular, MT architectures, both statistical and neural, can be successfully used for the task, but using supplementary monolingual data is not always as beneficial as using additional language data, contrarily to what is observed for MT.

IJCNLP-AACL 2021

Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task?

low resource machine translation

cognate prediction

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

Cognate prediction is the task of generating, in a given language, the likely cognates of words in a related language, where cognates are words in related languages that have evolved from a common ancestor word. It is a task for which little data exists and which can aid linguists in the discovery of previously undiscovered relations. Previous work has applied machine translation (MT) techniques to this task, based on the tasks' similarities, without, however, studying their numerous differences or optimising architectural choices and hyper-parameters. In this paper, we investigate whether cognate prediction can benefit from insights from low-resource MT. We first compare statistical MT (SMT) and neural MT (NMT) architectures in a bilingual setup. We then study the impact of employing data augmentation techniques commonly seen to give gains in low-resource MT: monolingual pretraining, backtranslation and multilinguality.
Our experiments on several Romance languages show that cognate prediction behaves only to a certain extent like a standard low-resource MT task. In particular, MT architectures, both statistical and neural, can be successfully used for the task, but using supplementary monolingual data is not always as beneficial as using additional language data, contrarily to what is observed for MT.

workshop paper

Streaming cross document entity coreference (CDC) systems disambiguate mentions of named entities in a scalable manner via incremental clustering.
Unlike other approaches for named entity disambiguation (e.g., entity linking), streaming CDC allows for the disambiguation of entities that are unknown at inference time.
Thus, it is well-suited for processing streams of data where new entities are frequently introduced.
Despite these benefits, this task is currently difficult to study, as existing approaches are either evaluated on datasets that are no longer available, or omit other crucial details needed to ensure fair comparison.
In this work, we address this issue by compiling a large benchmark adapted from existing free datasets, and performing a comprehensive evaluation of a number of novel and existing baseline models.
We investigate: how to best encode mentions, which clustering algorithms are most effective for grouping mentions, how models transfer to different domains, and how bounding the number of mentions tracked during inference impacts performance.
Our results show that the relative performance of neural and feature-based mention encoders varies across different domains, and in most cases the best performance is achieved using a combination of both approaches.
We also find that performance is minimally impacted by limiting the number of tracked mentions.

Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference

Temporal Knowledge Graphs (TKGs) have been developed and used in many different areas. Reasoning on TKGs that predicts potential facts (events) in the future brings great challenges to existing models. When facing a prediction task, human beings usually search useful historical information (i.e., clues) in their memories and then reason for future meticulously. Inspired by this mechanism, we propose CluSTeR to predict future facts in a two-stage manner, Clue Searching and Temporal Reasoning, accordingly. Specifically, at the clue searching stage, CluSTeR learns a beam search policy via reinforcement learning (RL) to induce multiple clues from historical facts. At the temporal reasoning stage, it adopts a graph convolution network based sequence method to deduce answers from clues. Experiments on four datasets demonstrate the substantial advantages of CluSTeR compared with the state-of-the-art methods. Moreover, the clues found by CluSTeR further provide interpretability for the results.

Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs

Generating high-quality arguments, while being challenging, may benefit a wide range of downstream applications, such as writing assistants and argument search engines. Motivated by the effectiveness of utilizing knowledge graphs for supporting general text generation tasks, this paper investigates the usage of argumentation-related knowledge graphs to control the generation of arguments. In particular, we construct and populate three knowledge graphs, employing several compositions of them to encode various knowledge into texts of debate portals and relevant paragraphs from Wikipedia. Then, the texts with the encoded knowledge are used to fine-tune a pre-trained text generation model, GPT-2. We evaluate the newly created arguments manually and automatically, based on several dimensions important in argumentative contexts, including argumentativeness and plausibility. The results demonstrate the positive impact of encoding the graphs' knowledge into debate portal texts for generating arguments with superior quality than those generated without knowledge.

Employing Argumentation Knowledge Graphs for Neural Argument Generation

TextFlint is a multilingual robustness evaluation toolkit for NLP tasks that incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analyses. This enables practitioners to automatically evaluate their models from various aspects or to customize their evaluations as desired with just a few lines of code. TextFlint also generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model in terms of its robustness. To guarantee acceptability, all the text transformations are linguistically based and all the transformed data selected (up to 100,000 texts) scored highly under human evaluation. To validate the utility, we performed large-scale empirical evaluations (over 67,000) on state-of-the-art deep learning models, classic supervised methods, and real-world systems. The toolkit is already available at https://github.com/textflint with all the evaluation results demonstrated at textflint.io.

TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing

In visual storytelling, a short story is generated based on a given image sequence. Despite years of work, most visual storytelling models remain limited in terms of the generated stories' fixed length: most models produce stories with exactly five sentences because five sentence stories dominate the training data. The fix-length stories carry limited details and provide ambiguous textual information to the readers. Therefore, we propose to "stretch" the stories, which create the potential to present in-depth visual details. This paper presents Stretch-VST, a visual storytelling framework that enables the generation of prolonged stories by adding appropriate knowledge, which is selected by the proposed scoring function. We propose a length-controlled Transformer to generate long stories. This model introduces novel positional encoding methods to maintain story quality with lengthy inputs. Experiments confirm that long stories are generated without deteriorating the quality. The human evaluation further shows that Stretch-VST can provide better focus and detail when stories are prolonged compared to the state of the art.

Stretch-VST: Getting Flexible With Visual Stories

We present Retriever-Transducer-Checker (ReTraCk), a neural semantic parsing frame-work for large scale knowledge base question answering (KBQA). ReTraCk is designed as a modular framework to maintain high flexibility. It includes a retriever to retrieve relevant KB items efficiently, a transducer to generate logical form with syntax correctness guarantees and a checker to improve the transduction procedure. ReTraCk is ranked at top1 overall performance on the GrailQA leaderboard and obtains highly competitive performance on the typical WebQuestionsSP benchmark. Our system can interact with users timely, demonstrating the efficiency of the proposed framework.

ReTraCk: A Flexible and Efficient Framework for Knowledge Base Question Answering

There have been several attempts to create an accurate and thorough emotion lexicon in English, which identifies the emotional content of words. Of the several commonly used resources, the NRC emotion lexicon has received the most attention due to its availability, size, and its choice of Plutchik's expressive 8-class emotion model. In this paper we identify a large number of troubling entries in the NRC lexicon, where words that should in most contexts be emotionally neutral, with no affect (e.g., lesbian, stone, mountain), are associated with emotional labels that are inaccurate, nonsensical, pejorative, or, at best, highly contingent and context-dependent (e.g., lesbian labeled as disgust and sadness, \emo stone as anger, or mountain as anticipation). We describe a procedure for semi-automatically correcting these problems in the NRC, which includes disambiguating POS categories and aligning NRC entries with other emotion lexicons to infer the accuracy of labels. We demonstrate via an experimental benchmark that the quality of the resources is thus improved. We release the revised resource and our code to enable other researchers to reproduce and build upon results.


Hell Hath No Fury? Correcting Bias in the NRC Emotion Lexicon

Community-level bans are a common tool against groups that enable online harassment and harmful speech. Unfortunately, the efficacy of community bans has only been partially studied and with mixed results. Here, we provide a flexible unsupervised methodology to identify in-group language and track user activity on Reddit both before and after the ban of a community (subreddit). We use a simple word frequency divergence to identify uncommon words overrepresented in a given community, not as a proxy for harmful speech but as a linguistic signature of the community. We apply our method to 15 banned subreddits, and find that community response is heterogeneous between subreddits and between users of a subreddit. Top users were more likely to become less active overall, while random users often reduced use of in-group language without decreasing activity. Finally, we find some evidence that the effectiveness of bans aligns with the content of a community. Users of dark humor communities were largely unaffected by bans while users of communities organized around white supremacy and fascism were the most affected. Altogether, our results show that bans do not affect all groups or users equally, and pave the way to understanding the effect of bans across communities.

Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task?

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Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference

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End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages

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Can Cognate Prediction Be Modelled as a Low-Resource Machine Translation Task?

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Next from IJCNLP-AACL 2021

Benchmarking Scalable Methods for Streaming Cross Document Entity Coreference

Similar lecture

End-to-End Lexically Constrained Machine Translation for Morphologically Rich Languages

Stay up to date with the latest Underline news!

PRESENTATIONS

CONFERENCES

COMPANY

RESOURCES

Downloads