Livestreaming videos have become an effective broadcasting method for both video sharing and educational purposes. However, livestreaming videos contain a considerable amount of off-topic content (i.e., up to 50%) which introduces significant noises and data load to downstream applications. This paper presents BehanceCC, a new human-annotated benchmark dataset for off-topic detection (also called chitchat detection) in livestreaming video transcripts. In addition to describing the challenges of the dataset, our extensive experiments of various baselines reveal the complexity of chitchat detection for livestreaming videos and suggest potential future research directions for this task. The dataset will be made publicly available to foster research in this area.

Question-Answer (QA) is one of the effective methods for storing knowledge which can be used for future retrieval. As such, identifying mentions of questions and their answers in text is necessary for a knowledge construction and retrieval systems. In the literature, QA identification has been well studied in the NLP community. However, most of the prior works are restricted to formal written documents such as papers or websites. As such, Questions and Answers that are presented in informal/noisy documents have not been adequately studied. One of the domains that can significantly benefit from QA identification is the domain of livestreaming video transcripts that involve abundant QA pairs to provide valuable knowledge for future users and services. Since video transcripts are often transcribed automatically for scale, they are prone to errors. Combined with the informal nature of discussion in a video, prior QA identification systems might not be able to perform well in this domain. To enable comprehensive research in this domain, we present a large-scale QA identification dataset annotated by human over transcripts of 500 hours of streamed videos. We employ Behance.net to collect the videos and their automatically obtained transcripts. Furthermore, we conduct extensive analysis on the annotated dataset to understand the complexity of QA identification for livestreaming video transcripts. Our experiments show that the annotated dataset presents unique challenges for existing methods and more research is necessary to explore more effective methods. The dataset and the models developed in this work will be publicly released for future research.

Machine translation (MT) is an important task in natural language processing, which aims to translate a sentence in a source language to another sentence with the same/similar semantics in a target language. Despite the huge effort on building MT systems for different language pairs, most previous work focuses on formal-language settings, where text to be translated come from written sources such as books and news articles. As a result, such MT systems could fail to translate livestreaming video transcripts, where text is often shorter and might be grammatically incorrect. To overcome this issue, we introduce a novel MT corpus - BehanceMT for livestreaming video transcript translation. Our corpus contains parallel transcripts for 3 language pairs, where English is the source language and Spanish, Chinese, and Arabic are the target languages. Experimental results show that finetuning a pretrained MT model on BehanceMT significantly improves the performance of the model in translating video transcripts across 3 language pairs. In addition, the finetuned MT model outperforms GoogleTranslate in 2 out of 3 language pairs, further demonstrating the usefulness of our proposed dataset for video transcript translation. BehanceMT will be publicly released upon the acceptance of the paper.

We describe Symlink, a SemEval shared task of extracting mathematical symbols and their descriptions from LaTeX source of scientific documents. This is a new task in SemEval 2022, which attracted 180 individual registrations and 59 final submissions from 7 participant teams. We expect the data developed for this task and the findings reported to be valuable for the scientific knowledge extraction and automated knowledge base construction communities. The data used in this task is publicly accessible at https://github.com/nlp-oregon/symlink.

Toxic span detection is the task of recognizing offensive spans in a text snippet. Although there has been prior work on classifying text snippets as offensive or not, the task of recognizing spans responsible for the toxicity of a text is not explored yet. In this work, we introduce a novel multi-task framework for toxic span detection in which the model seeks to simultaneously predict offensive words and opinion phrases to leverage their inter-dependencies and improve the performance. Moreover, we introduce a novel regularization mechanism to encourage the consistency of the model predictions across similar inputs for toxic span detection. Our extensive experiments demonstrate the effectiveness of the proposed model compared to strong baselines.

Event Argument Extraction (EAE) is one of the sub-tasks of event extraction, aiming to recognize the role of each entity mention toward a specific event trigger. Despite the success of prior works in sentence-level EAE, the document-level setting is less explored. In particular, whereas syntactic structures of sentences have been shown to be effective for sentence-level EAE, prior document-level EAE models totally ignore syntactic structures for documents. Hence, in this work, we study the importance of syntactic structures in document-level EAE. Specifically, we propose to employ Optimal Transport (OT) to induce structures of documents based on sentence-level syntactic structures and tailored to EAE task. Furthermore, we propose a novel regularization technique to explicitly constrain the contributions of unrelated context words in the final prediction for EAE. We perform extensive experiments on the benchmark document-level EAE dataset RAMS that leads to the state-of-the-art performance. Moreover, our experiments on the ACE 2005 dataset reveals the effectiveness of the proposed model in the sentence-level EAE by establishing new state-of-the-art results.

Given the increasing number of livestreaming videos, automatic speech recognition and post-processing for livestreaming video transcripts are crucial for efficient data management as well as knowledge mining. A key step in this process is punctuation restoration which restores fundamental text structures such as phrase and sentence boundaries from the video transcripts. This work presents a new human-annotated corpus, called BehancePR, for punctuation restoration in livestreaming video transcripts. Our experiments on BehancePR demonstrate the challenges of punctuation restoration for this domain. Furthermore, we show that popular natural language processing toolkits like Stanford Stanza, Spacy, and Trankit underperform on detecting sentence boundary on non-punctuated transcripts of livestreaming videos. The dataset is publicly accessible at http://github.com/nlp-uoregon/behancepr.

Suicide is a serious problem in every society. Understanding life events of a potential patient is essential for successful suicide-risk assessment and prevention. In this work, we focus on the Event Detection (ED) task to identify event trigger words of suicide-related events in public posts of discussion forums. In particular, we introduce SuicideED: a new dataset for the ED task that features seven suicidal event types to comprehensively capture suicide actions and ideation, and general risk and protective factors. Our experiments with current state-of-the-art ED systems suggest that this domain poses meaningful challenges as there is significant room for improvement of ED models. We will release SuicideED to support future research in this important area.

This paper presents our entry to the CreativeSumm 2022 shared task. Specifically tackling the problem of prime-time television screenplay summarization based on the SummScreen Forever Dreaming dataset. Our approach utilizes extended Longformers combined with sketch supervision including categories specifically for scene descriptions. Our system was able to produce the shortest summaries out of all submissions. While some problems with factual consistency still remain, the system was scoring highest among competitors in the ROUGE and BERTScore evaluation categories.

Event Detection (ED) aims to identify mentions/triggers of real world events in text. In the literature, this task is modeled as a sequence-labeling or word-prediction problem. In this work, we present a novel formulation in which ED is modeled as a word-label alignment task. In particular, given the words in a sentence and possible event types, the objective is to infer an alignment matrix in which event trigger words are aligned with the most likely event types. Moreover, we show that this new perspective facilitates the incorporation of word-label alignment biases to improve alignment matrix for ED. Novel alignment biases and Optimal Transport are introduced to solve our alignment problem for ED. We conduct experiments on a benchmark dataset to demonstrate the effectiveness of the proposed model for ED.

An important problem of Information Extraction involves Event Causality Identification (ECI) that seeks to identify causal relation between pairs of event mentions. Prior models for ECI have mainly solved the problem using the classification framework that does not explore prediction/generation of important context words from input sentences for causal recognition. In this work, we consider the words along the dependency path between the two event mentions in the dependency tree as the important context words for ECI. We introduce dependency path generation as a complementary task for ECI, which can be solved jointly with causal label prediction to improve the performance. To facilitate the multi-task learning, we cast ECI into a generation problem that aims to generate both causal relation and dependency path words from input sentence. In addition, we propose to use the REINFORCE algorithm to train our generative model where novel reward functions are designed to capture both causal prediction accuracy and generation quality. The experiments on two benchmark datasets demonstrate state-of-the-art performance of the proposed model for ECI.

Event Detection (ED) is the task of identifying and classifying trigger words of event mentions in text. Despite considerable research efforts in recent years for English text, the task of ED in other languages has been significantly less explored. Switching to non-English languages, important research questions for ED include how well existing ED models perform on different languages, how challenging ED is in other languages, and how well ED knowledge and annotation can be transferred across languages. To answer those questions, it is crucial to obtain multilingual ED datasets that provide consistent event annotation for multiple languages. There exist some multilingual ED datasets; however, they tend to cover a handful of languages and mainly focus on popular ones. Many languages are not covered in existing multilingual ED datasets. In addition, the current datasets are often small and not accessible to the public. To overcome those shortcomings, we introduce a new large-scale multilingual dataset for ED (called MINION) that consistently annotates events for 8 different languages; 5 of them have not been supported by existing multilingual datasets. We also perform extensive experiments and analysis to demonstrate the challenges and transferability of ED across languages in MINION that in all call for more research effort in this area. We will release the dataset to promote future research on multilingual ED.

Event trigger detection, entity mention recognition, event argument extraction, and relation extraction are the four important tasks in information extraction that have been performed jointly (Joint Information Extraction - JointIE) to avoid error propagation and leverage dependencies between the task instances (i.e., event triggers, entity mentions, relations, and event arguments). However, previous JointIE models often assume heuristic manually-designed dependency between the task instances and mean-field factorization for the joint distribution of instance labels, thus unable to capture optimal dependencies among instances and labels to improve representation learning and IE performance. To overcome these limitations, we propose to induce a dependency graph among task instances from data to boost representation learning. To better capture dependencies between instance labels, we propose to directly estimate their joint distribution via Conditional Random Fields. Noise Contrastive Estimation is introduced to address the maximization of the intractable joint likelihood for model training. Finally, to improve the decoding with greedy or beam search in prior work, we present Simulated Annealing to better find the globally optimal assignment for instance labels at decoding time. Experimental results show that our proposed model outperforms previous models on multiple IE tasks across 5 datasets and 2 languages.

In this work, we focus on Cross-Lingual Event Detection where a model is trained on data from a source language but its performance is evaluated on data from a second, target, language. Most recent works in this area have harnessed the language-invariant qualities displayed by pre-trained Multi-lingual Language Models. Their performance, however, reveals there is room for improvement as the cross-lingual setting entails particular challenges. We employ Adversarial Language Adaptation to train a Language Discriminator to discern between the source and target languages using unlabeled data. The discriminator is trained in an adversarial manner so that the encoder learns to produce refined, language-invariant representations that lead to improved performance. More importantly, we optimize the adversarial training process by only presenting the discriminator with the most informative samples. We base our intuition about what makes a sample informative on two disparate metrics: sample similarity and event presence. Thus, we propose leveraging Optimal Transport as a solution to naturally combine these two distinct information sources into the selection process. Extensive experiments on 8 different language pairs, using 4 languages from unrelated families, show the flexibility and effectiveness of our model that achieves state-of-the-art results.

This paper presents FAMIE, a comprehensive and efficient active learning (AL) toolkit for multilingual information extraction. FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration. This hinders the engagement, productivity, and efficiency of annotators. Based on the idea of using a small proxy network for fast data selection, we introduce a novel knowledge distillation mechanism to synchronize the proxy network with the main large model (i.e., BERT-based) to ensure the appropriateness of the selected annotation examples for the main model. Our AL framework can support multiple languages. The experiments demonstrate the advantages of FAMIE in terms of competitive performance and time efficiency for sequence labeling with AL. We publicly release our code (https://github.com/nlp-uoregon/famie) and demo website (http://nlp.uoregon.edu:9000/). A demo video for FAMIE is provided at: https://youtu.be/I2i8n_jAyrY

Traditional event detection classifies a word or a phrase in a given sentence for a set of prede- fined event types. The limitation of such pre- defined set is that it prevents the adaptation of the event detection models to new event types. We study a novel formulation of event detec- tion that describes types via several keywords to match the contexts in documents. This fa- cilitates the operation of the models to new types. We introduce a novel feature-based attention mechanism for convolutional neural networks for event detection in the new for- mulation. Our extensive experiments demon- strate the benefits of the new formulation for new type extension for event detection as well as the proposed attention mechanism for this problem