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d258833483 | In this paper, we propose DIFFUSIONNER, which formulates the named entity recognition task as a boundary-denoising diffusion process and thus generates named entities from noisy spans. During training, DIFFUSIONNER gradually adds noises to the golden entity boundaries by a fixed forward diffusion process and learns a reverse diffusion process to recover the entity boundaries. In inference, DIFFU-SIONNER first randomly samples some noisy spans from a standard Gaussian distribution and then generates the named entities by denoising them with the learned reverse diffusion process. The proposed boundary-denoising diffusion process allows progressive refinement and dynamic sampling of entities, empowering DIFFUSIONNER with efficient and flexible entity generation capability. Experiments on multiple flat and nested NER datasets demonstrate that DIFFUSIONNER achieves comparable or even better performance than previous state-of-the-art models 1 . | DiffusionNER: Boundary Diffusion for Named Entity Recognition |
d53083689 | Deep neural networks have been displaying superior performance over traditional supervised classifiers in text classification. They learn to extract useful features automatically when sufficient amount of data is presented. However, along with the growth in the number of documents comes the increase in the number of categories, which often results in poor performance of the multiclass classifiers. In this work, we use external knowledge in the form of topic category taxonomies to aide the classification by introducing a deep hierarchical neural attention-based classifier. Our model performs better than or comparable to state-of-the-art hierarchical models at significantly lower computational cost while maintaining high interpretability. | A Hierarchical Neural Attention-based Text Classifier |
d252819067 | Most previous studies on temporal relation extraction focus on extracting temporal relations among events and suffer from the issue of different forms of events, timexes and Document Creation Time (DCT) in a document. Moreover, DCT can act as a hub to semantically connect the other events and timexes. Unfortunately, previous work cannot fully use such critical and helpful information. To address the above issues, we propose a unified DCTcentered Temporal Relation Extraction model DTRE to identify temporal relations among events, timexes and DCT. Specifically, we first introduce sentence-style DCT to unify the expressions of event, timex and DCT. Then, we apply a DCT-aware graph to obtain their contextual structural representations. Furthermore, we propose a DCT-anchoring multi-task framework to jointly predict three tasks of temporal relation extraction in a batch. Finally, we provide a DCT-guided global inference to further enhance the global consistency among different relations. Experimental results on three popular datasets TBD, TDD-man and TDD-Auto show that our DTRE outperforms several SOTA baselines on E-E, E-T and E-D significantly. | DCT-Centered Temporal Relation Extraction |
d261494520 | We present the most relevant results of the project MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages in its second year. Parallel and monolingual corpora have been produced for eleven lowresourced European languages by crawling large amounts of textual data from selected top-level domains of the Internet; both human and automatic evaluation show its usefulness. In addition, several large language models pretrained on MaCoCu data have been published, as well as the code used to collect and curate the data. | MaCoCu: Massive collection and curation of monolingual and bilingual data: focus on under-resourced languages |
d259076187 | In this project, we have investigated the use of advanced machine learning methods, specifically fine-tuned large language models, for pre-annotating data for a lexical extension task, namely adding descriptive words (verbs) to an existing (but incomplete, as of yet) ontology of event types. Several research questions have been focused on, from the investigation of a possible heuristics to provide at least hints to annotators which verbs to include and which are outside the current version of the ontology, to the possible use of the automatic scores to help the annotators to be more efficient in finding a threshold for identifying verbs that cannot be assigned to any existing class and therefore they are to be used as seeds for a new class. We have also carefully examined the correlation of the automatic scores with the human annotation. While the correlation turned out to be strong, its influence on the annotation proper is modest due to its near linearity, even though the mere fact of such pre-annotation leads to relatively short annotation times. | Extending an Event-type Ontology: Adding Verbs and Classes Using Fine-tuned LLMs Suggestions |
d21691768 | We introduce TED-Multilingual Discourse Bank, a corpus of TED talks transcripts in 6 languages (English, German, Polish, European Portuguese, Russian and Turkish), where the ultimate aim is to provide a clearly described level of discourse structure and semantics in multiple languages. The corpus is manually annotated following the goals and principles of PDTB, involving explicit and implicit discourse connectives, entity relations, alternative lexicalizations and no relations. In the corpus, we also aim to capture the characteristics of spoken language that exist in the transcripts and adapt the PDTB scheme according to our aims; for example, we introduce hypophora. We spot other aspects of spoken discourse such as the discourse marker use of connectives to keep them distinct from their discourse connective use. TED-MDB is, to the best of our knowledge, one of the few multilingual discourse treebanks and is hoped to be a source of parallel data for contrastive linguistic analysis as well as language technology applications. We describe the corpus, the annotation procedure and provide preliminary corpus statistics. | Multilingual Extension of PDTB-Style Annotation: The Case of TED Multilingual Discourse Bank |
d202540839 | Zero-shot text classification (0SHOT-TC) is a challenging NLU problem to which little attention has been paid by the research community. 0SHOT-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.g., topic, emotion, event, etc.) described by the label. And there are only a few articles studying 0SHOT-TC, all focusing only on topical categorization which, we argue, is just the tip of the iceberg in 0SHOT-TC. In addition, the chaotic experiments in literature make no uniform comparison, which blurs the progress.This work benchmarks the 0SHOT-TC problem by providing unified datasets, standardized evaluations, and state-of-the-art baselines. Our contributions include: i) The datasets we provide facilitate studying 0SHOT-TC relative to conceptually different and diverse aspects: the "topic" aspect includes "sports" and "politics" as labels; the "emotion" aspect includes "joy" and "anger"; the "situation" aspect includes "medical assistance" and "water shortage". ii) We extend the existing evaluation setup (labelpartially-unseen) -given a dataset, train on some labels, test on all labels -to include a more challenging yet realistic evaluation label-fully-unseen 0SHOT-TC(Chang et al., 2008), aiming at classifying text snippets without seeing task specific training data at all. iii) We unify the 0SHOT-TC of diverse aspects within a textual entailment formulation and study it this way. 1The plague in Mongolia, occurring last week, has caused more than a thousand isolation health, | Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach |
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d261342128 | Overview of CCL23-Eval Task 1: Named Entity Recognition in Ancient Chinese Books 織繨繥 縲縲繮繤 繃繨繩繮繡 繎繡繴繩繯繮繡繬 繃繯繮繦繥繲繥繮繣繥 繯繮 繃繯繭繰繵繴繡繴繩繯繮繡繬 繌繩繮繧繵繩繳繴繩繣繳 縨繃繃繌縲縰縲縳縩 繰繲繥縭 繳繥繮繴繥繤 縱縰 繥繶繡繬繵繡繴繩繯繮 繴繡繳繫繳 繩繮 繴繨繥 縌繥繬繤 繯繦 繃繨繩繮繥繳繥 繩繮繦繯繲繭繡繴繩繯繮 繰繲繯繣繥繳繳繩繮繧縮 繁繭繯繮繧 繴繨繥繭縬 織繡繳繫 縱 縨繇繵繎繅繒縲縰縲縳縩 繦繯繣繵繳繥繤 繯繮 繴繨繥 繥繶繡繬繵繡繴繩繯繮 繯繦 繎繡繭繥繤 繅繮繴繩繴繹 繒繥繣繯繧繮繩繴繩繯繮 縨繎繅繒縩 繦繯繲 繁繮繣繩繥繮繴 繃繨繩繮繥繳繥 繴繥繸繴繳縬 繯繲繧繡繮繩繺繥繤 繢繹 繴繨繥 繄繩繧繩繴繡繬 繈繵繭繡繮繴繩繥繳 繒繥繳繥繡繲繣繨 繃繥繮繴繥繲 繡繮繤 繴繨繥 繉繮繳繴繩繴繵繴繥 繯繦 繁繲繴繩縌繣繩繡繬 繉繮繴繥繬繬繩繧繥繮繣繥 繡繴 繐繥繫繩繮繧 繕繮繩繶繥繲繳繩繴繹縮 織繨繥 繭繡繩繮 繯繢繪繥繣繴繩繶繥 繯繦 繴繨繩繳 繴繡繳繫 繷繡繳 繴繯 繡繵繴繯繭繡繴繩繣繡繬繬繹 繩繤繥繮繴繩繦繹 繩繭繰繯繲繴繡繮繴 繥繮繴繩繴繩繥繳 繲繥繬繡繴繥繤 繴繯 繴繨繥 繢繡繳繩繣 繣繯繭繰繯繮繥繮繴繳 繯繦 繥繶繥繮繴繳 繩繮 繡繮繣繩繥繮繴 繴繥繸繴繳縬 繴繨繵繳 繰繲繯繶繩繤繩繮繧 繡 繦繯繵繮繤繡繴繩繯繮 繦繯繲 繡繮繡繬繹繺繩繮繧 繡繮繤 繰繲繯繣繥繳繳繩繮繧 繃繬繡繳繳繩繣繡繬 繃繨繩繮繥繳繥 繴繥繸繴繳縮 織繨繥 繥繶繡繬繵繡繴繩繯繮 繲繥繬繥繡繳繥繤 繴繨繥 Twenty-four Histories 繤繡繴繡繳繥繴縬 繷繨繩繣繨 繣繯繶繥繲繳 繭繵繬繴繩繰繬繥 繤繹繮繡繳繴繩繥繳 繡繮繤 繤繯繭繡繩繮繳縬 繩繮繣繬繵繤繩繮繧 繴繨繲繥繥 繴繹繰繥繳 繯繦 繥繮繴繩繴繩繥繳縺 繰繥繲縭 繳繯繮繡繬 繮繡繭繥繳縬 繢繯繯繫 繴繩繴繬繥繳縬 繡繮繤 繯縎繣繩繡繬 繰繯繳繩繴繩繯繮繳縮 織繷繯 繴繲繡繣繫繳縬 繲繥繳繴繲繩繣繴繥繤 繡繮繤 繵繮繲繥繳繴繲繩繣繴繥繤 繴繲繡繣繫繳縬 繷繥繲繥 繳繥繴 繵繰 繴繯 繡繳繳繥繳繳 繴繨繥 繣繡繰繡繢繩繬繩繴繩繥繳 繯繦 繰繲繥縭繴繲繡繩繮繥繤 繭繯繤繥繬繳 繷繩繴繨 繤繩縋繥繲繥繮繴 繳繰繥繣繩縌縭 繣繡繴繩繯繮繳縮 繁 繴繯繴繡繬 繯繦 縱縲縷 繴繥繡繭繳 繲繥繧繩繳繴繥繲繥繤 繦繯繲 繴繨繩繳 繥繶繡繬繵繡繴繩繯繮 繴繡繳繫縮 繉繮 繴繨繥 繲繥繳繴繲繩繣繴繥繤 繴繲繡繣繫縬 繴繨繥 繢繥繳繴縭繰繥繲繦繯繲繭繩繮繧 繳繹繳繴繥繭 繡繣繨繩繥繶繥繤 繡繮 繆縱 繳繣繯繲繥 繯繦 縹縶縮縱縵縥 繯繮 繴繨繥 繴繥繳繴 繳繥繴，繷繨繩繬繥 繩繮 繴繨繥 繵繮繲繥繳繴繲繩繣繴繥繤 繴繲繡繣繫縬 繴繨繥 繨繩繧繨繥繳繴 繰繥繲繦繯繲繭繡繮繣繥 繲繥繳繥繡繲繣繨繥繤 繡繮 繆縱 繳繣繯繲繥 繯繦 縹縵縮縴縸縥縮 | |
d257505127 | CB2 is a multi-agent platform to study collaborative natural language interaction in a grounded task-oriented scenario. It includes a 3D game environment, a backend server designed to serve trained models to human agents, and various tools and processes to enable scalable studies. We deploy CB2 at https://cb2.ai as a system demonstration with a learned instruction following model. | CB2: Collaborative Natural Language Interaction Research Platform |
d251223486 | Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, automatically extracting 413,299 tasks from internet tables -orders of magnitude more than the next-largest public datasets. Finetuning on the resulting dataset leads to improved FSL performance on Natural Language Processing (NLP) tasks, but not proportionally to dataset scale. In fact, we find that narrow subsets of our dataset sometimes outperform more diverse datasets. For example, finetuning on software documentation from support.google.com raises FSL performance by a mean of +7.5% on 52 downstream tasks, which beats training on 40 humancurated NLP datasets (+6.7%). Finetuning on various narrow datasets leads to similar broad improvements across test tasks, suggesting that the gains are not from domain adaptation but adapting to FSL in general. We do not observe clear patterns between the datasets that lead to FSL gains, leaving open questions about why certain data helps with FSL.Omer Levy, Minjoon Seo, Eunsol Choi, and Luke Zettlemoyer. 2017. Zero-shot relation extraction via reading comprehension. In CoNLL. | Few-shot Adaptation Works with UnpredicTable Data |
d239885682 | During the fine-tuning phase of transfer learning, the pretrained vocabulary remains unchanged, while model parameters are updated. The vocabulary generated based on the pretrained data is suboptimal for downstream data when domain discrepancy exists. We propose to consider the vocabulary as an optimizable parameter, allowing us to update the vocabulary by expanding it with domain-specific vocabulary based on a tokenization statistic. Furthermore, we preserve the embeddings of the added words from overfitting to downstream data by utilizing knowledge learned from a pretrained language model with a regularization term. Our method achieved consistent performance improvements on diverse domains (i.e., biomedical, computer science, news, and reviews). | AVocaDo: Strategy for Adapting Vocabulary to Downstream Domain |
d15210695 | We propose a general method to watermark and probabilistically identify the structured outputs of machine learning algorithms. Our method is robust to local editing operations and provides well defined trade-offs between the ability to identify algorithm outputs and the quality of the watermarked output. Unlike previous work in the field, our approach does not rely on controlling the inputs to the algorithm and provides probabilistic guarantees on the ability to identify collections of results from one's own algorithm. We present an application in statistical machine translation, where machine translated output is watermarked at minimal loss in translation quality and detected with high recall. | Watermarking the Outputs of Structured Prediction with an application in Statistical Machine Translation |
d252411707 | We provide a novel dataset -DiaWUG -with judgements on diatopic lexical semantic variation for six Spanish variants in Europe and Latin America. In contrast to most previous meaning-based resources and studies on semantic diatopic variation, we collect annotations on semantic relatedness for Spanish target words in their contexts from both a semasiological perspective (i.e., exploring the meanings of a word given its form, thus including polysemy) and an onomasiological perspective (i.e., exploring identical meanings of words with different forms, thus including synonymy). In addition, our novel dataset exploits and extends the existing framework DURel for annotating word senses in context(Erk et al., 2013;Schlechtweg et al., 2018)and the framework-embedded Word Usage Graphs (WUGs) -which up to now have mainly be used for semasiological tasks and resources -in order to distinguish, visualize and interpret lexical semantic variation of contextualized words in Spanish from these two perspectives, i.e., semasiological and onomasiological language variation. | DiaWUG: A Dataset for Diatopic Lexical Semantic Variation in Spanish |
d28938268 | Sentiment Analysis systems aims at detecting opinions and sentiments that are expressed in texts. Many approaches in literature are based on resources that model the prior polarity of words or multi-word expressions, i.e. a polarity lexicon. Such resources are defined by teams of annotators, i.e. a manual annotation is provided to associate emotional or sentiment facets to the lexicon entries. The development of such lexicons is an expensive and language dependent process, making their coverage of linguistic sentiment phenomena limited. Moreover, once a lexicon is defined it can hardly be adopted in a different language or even a different domain. In this paper, we present several Distributional Polarity Lexicons (DPLs), i.e. large-scale polarity lexicons acquired with an unsupervised methodology based on Distributional Models of Lexical Semantics. Given a set of heuristically annotated sentences from Twitter, we transfer the sentiment information from sentences to words. The approach is mostly unsupervised, and experimental evaluations on Sentiment Analysis tasks in two languages show the benefits of the generated resources. The generated DPLs are publicly available in English and Italian. | A Language Independent Method for Generating Large Scale Polarity Lexicons |
d2247967 | The intersection of tree transducer-based translation models with n-gram language models results in huge dynamic programs for machine translation decoding. We propose a multipass, coarse-to-fine approach in which the language model complexity is incrementally introduced. In contrast to previous orderbased bigram-to-trigram approaches, we focus on encoding-based methods, which use a clustered encoding of the target language. Across various encoding schemes, and for multiple language pairs, we show speed-ups of up to 50 times over single-pass decoding while improving BLEU score. Moreover, our entire decoding cascade for trigram language models is faster than the corresponding bigram pass alone of a bigram-to-trigram decoder. | Coarse-to-Fine Syntactic Machine Translation using Language Projections |
d236477336 | Multi-hop question generation requires complex reasoning and coherent language realization. Learning a generation model for the problem requires extensive multi-hop question answering (QA) data, which are limited due to the manual collection effort. A two-phase strategy addresses the insufficiency of multihop QA data by first generating and then composing single-hop sub-questions. Learning this generating and then composing twophase model, however, requires manually labeled question decomposition data, which is labor intensive. To overcome this limitation, we propose a novel generative approach that optimizes the two-phase model without question decomposition data. We treat the unobserved sub-questions as latent variables and propose an objective that estimates the true sub-questions via variational inference. We further generalize the generative modeling to single-hop QA data. We hypothesize that each single-hop question is a sub-question of an unobserved multi-hop question, and propose an objective that generates single-hop questions by decomposing latent multi-hop questions. We show that the two objectives can be unified and both optimize the two-phase generation model. Experiments show that the proposed approach outperforms competitive baselines on HOTPOTQA, a benchmark multi-hop question answering dataset.Reasoning Progress< Dario Franchitti , contracted by , AMG > < Dario Franchitti , competed in , DTM > < AMG , headquartered in , Affalterbach, | Latent Reasoning for Low-Resource Question Generation |
d250391030 | Most of the published approaches and resources for offensive language and hate speech detection are tailored for the English language. In consequence, cross-lingual and cross-cultural perspectives lack some essential resources. The lack of diversity of the datasets in Spanish is notable. Variations throughout Spanish-speaking countries make existing datasets not enough to encompass the task in the different Spanish variants. We manually annotated 9834 tweets from Chile to enrich the existing Spanish resources with different words and new targets of hate that have not been considered in previous studies. We conducted several cross-dataset evaluation experiments of the models published in the literature using our Chilean dataset and two others in English and Spanish. We propose a comparative framework for quickly conducting comparative experiments using different previously published models. In addition, we set up a Codalab competition for further comparison of new models in a standard scenario, that is, data partitions and evaluation metrics. All resources can be accessed through a centralized repository for researchers to get a complete picture of the progress on the multilingual hate speech and offensive language detection task. | Multilingual Resources for Offensive Language Detection |
d16240382 | This paper describes a unified neural architecture for identifying and classifying multi-typed semantic relations between words in a sentence. We investigate two typical and well-studied tasks: semantic role labeling (SRL) which identifies the relations between predicates and arguments, and relation classification (RC) which focuses on the relation between two entities or nominals. While mostly studied separately in prior work, we show that the two tasks can be effectively connected and modeled using a general architecture. Experiments on CoNLL-2009 benchmark datasets show that our SRL models significantly outperform state-of-the-art approaches. Our RC models also yield competitive performance with the best published records. Furthermore, we show that the two tasks can be trained jointly with multi-task learning, resulting in additive significant improvements for SRL. † Corresponding author: Wanxiang Che This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/ | A Unified Architecture for Semantic Role Labeling and Relation Classification |
d215238664 | Recently, BERT has become an essential ingredient of various NLP deep models due to its effectiveness and universal-usability. However, the online deployment of BERT is often blocked by its large-scale parameters and high computational cost. There are plenty of studies showing that the knowledge distillation is efficient in transferring the knowledge from BERT into the model with a smaller size of parameters. Nevertheless, current BERT distillation approaches mainly focus on taskspecified distillation, such methodologies lead to the loss of the general semantic knowledge of BERT for universal-usability. In this paper, we propose a sentence representation approximating oriented distillation framework that can distill the pre-trained BERT into a simple LSTM based model without specifying tasks. Consistent with BERT, our distilled model is able to perform transfer learning via fine-tuning to adapt to any sentencelevel downstream task. Besides, our model can further cooperate with task-specific distillation procedures. The experimental results on multiple NLP tasks from the GLUE benchmark show that our approach outperforms other taskspecific distillation methods or even much larger models, i.e., ELMO, with efficiency well-improved. | Towards Non-task-specific Distillation of BERT via Sentence Representation Approximation |
d252918091 | Multimodal machine translation (MMT) aims to improve translation quality by equipping the source sentence with its corresponding image. Despite the promising performance, MMT models still suffer the problem of input degradation: models focus more on textual information while visual information is generally overlooked. In this paper, we endeavor to improve MMT performance by increasing visual awareness from an information theoretic perspective. In detail, we decompose the informative visual signals into two parts: source-specific information and target-specific information. We use mutual information to quantify them and propose two methods for objective optimization to better leverage visual signals. Experiments on two datasets demonstrate that our approach can effectively enhance the visual awareness of MMT model and achieve superior results against strong baselines. | Increasing Visual Awareness in Multimodal Neural Machine Translation from an Information Theoretic Perspective |
d258587920 | The ever-increasing size of language models curtails their widespread availability to the community, thereby galvanizing many companies into offering access to large language models through APIs. One particular type, suitable for dense retrieval, is a semantic embedding service that builds vector representations of input text. With a growing number of publicly available APIs, our goal in this paper is to analyze existing offerings in realistic retrieval scenarios, to assist practitioners and researchers in finding suitable services according to their needs. Specifically, we investigate the capabilities of existing semantic embedding APIs on domain generalization and multilingual retrieval. For this purpose, we evaluate these services on two standard benchmarks, BEIR and MIRACL. We find that re-ranking BM25 results using the APIs is a budget-friendly approach and is most effective in English, in contrast to the standard practice of employing them as first-stage retrievers. For non-English retrieval, re-ranking still improves the results, but a hybrid model with BM25 works best, albeit at a higher cost. We hope our work lays the groundwork for evaluating semantic embedding APIs that are critical in search and more broadly, for information access. | Evaluating Embedding APIs for Information Retrieval |
d261211338 | We investigate and refine denoising methods for NER task on data that potentially contains extremely noisy labels from multi-sources. In this paper, we first summarized all possible noise types and noise generation schemes, based on which we built a thorough evaluation system. We then pinpoint the bottleneck of current state-of-art denoising methods using our evaluation system. Correspondingly, we propose several refinements, including using a twostage framework to avoid error accumulation; a novel confidence score utilizing minimal clean supervision to increase predictive power; an automatic cutoff fitting to save extensive hyperparameter tuning; a warm started weighted partial CRF to better learn on the noisy tokens. Additionally, we propose to use adaptive sampling to further boost the performance in long-tailed entity settings. Our method improves F1 score by on average at least 5 ∼ 10% over current state-of-art across extensive experiments. | UseClean: learning from complex noisy labels in named entity recognition |
d258967342 | Accurate neural models are much less efficient than non-neural models and are useless for processing billions of social media posts or handling user queries in real time with a limited budget. This study revisits the fastest patternbased NLP methods to make them as accurate as possible, thus yielding a strikingly simple yet surprisingly accurate morphological analyzer for Japanese. The proposed method induces reliable patterns from a morphological dictionary and annotated data. Experimental results on two standard datasets confirm that the method exhibits comparable accuracy to learning-based baselines, while boasting a remarkable throughput of over 1,000,000 sentences per second on a single modern CPU. The source code is available at https://www.tkl.iis.u-tokyo. ac.jp/~ynaga/jagger/. | Back to Patterns: Efficient Japanese Morphological Analysis with Feature-Sequence Trie |
d218974092 | This paper presents experiments on sentence boundary detection in transcripts of spoken dialogues. Segmenting spoken language into sentence-like units is a challenging task, due to disfluencies, ungrammatical or fragmented structures and the lack of punctuation. In addition, one of the main bottlenecks for many NLP applications for spoken language is the small size of the training data, as the transcription and annotation of spoken language is by far more time-consuming and labour-intensive than processing written language. We therefore investigate the benefits of data expansion and transfer learning and test different ML architectures for this task. Our results show that data expansion is not straightforward and even data from the same domain does not always improve results. They also highlight the importance of modelling, i.e. of finding the best architecture and data representation for the task at hand. For the detection of boundaries in spoken language transcripts, we achieve a substantial improvement when framing the boundary detection problem as a sentence pair classification task, as compared to a sequence tagging approach. | Improving Sentence Boundary Detection for Spoken Language Transcripts |
d253420317 | Image captioning models tend to describe images in an object-centric way, emphasising visible objects. But image descriptions can also abstract away from objects and describe the type of scene depicted. In this paper, we explore the potential of a state of the art Vision and Language model, VinVL, to caption images at the scene level using (1) a novel dataset which pairs images with both object-centric and scene descriptions. Through (2) an in-depth analysis of the effect of the fine-tuning, we show (3) that a small amount of curated data suffices to generate scene descriptions without losing the capability to identify object-level concepts in the scene; the model acquires a more holistic view of the image compared to when objectcentric descriptions are generated. We discuss the parallels between these results and insights from computational and cognitive science research on scene perception. | Understanding Cross-modal Interactions in V&L Models that Generate Scene Descriptions |
d52910554 | Causal understanding is essential for many kinds of decision-making, but causal inference from observational data has typically only been applied to structured, low-dimensional datasets.While text classifiers produce low-dimensional outputs, their use in causal inference has not previously been studied.To facilitate causal analyses based on language data, we consider the role that text classifiers can play in causal inference through established modeling mechanisms from the causality literature on missing data and measurement error. We demonstrate how to conduct causal analyses using text classifiers on simulated and Yelp data, and discuss the opportunities and challenges of future work that uses text data in causal inference. | Challenges of Using Text Classifiers for Causal Inference |
d226284017 | Automated generation of medical reports that describe the findings in the medical images helps radiologists by alleviating their workload. Medical report generation system should generate correct and concise reports. However, data imbalance makes it difficult to train models accurately. Medical datasets are commonly imbalanced in their finding labels because incidence rates differ among diseases; moreover, the ratios of abnormalities to normalities are significantly imbalanced. We propose a novel reinforcement learning method with a reconstructor to improve the clinical correctness of generated reports to train the data-to-text module with a highly imbalanced dataset. Moreover, we introduce a novel data augmentation strategy for reinforcement learning to additionally train the model on infrequent findings. From the perspective of a practical use, we employ a Two-Stage Medical Report Generator (TS-MRGen) for controllable report generation from input images. TS-MRGen consists of two separated stages: an image diagnosis module and a data-to-text module. Radiologists can modify the image diagnosis module results to control the reports that the data-totext module generates. We conduct an experiment with two medical datasets to assess the data-to-text module and the entire two-stage model. Results demonstrate that the reports generated by our model describe the findings in the input image more correctly. | Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation |
d7075805 | In this paper, we describe a sourceside reordering method based on syntactic chunks for phrase-based statistical machine translation. First, we shallow parse the source language sentences. Then, reordering rules are automatically learned from source-side chunks and word alignments. During translation, the rules are used to generate a reordering lattice for each sentence. Experimental results are reported for a Chinese-to-English task, showing an improvement of 0.5%-1.8% BLEU score absolute on various test sets and better computational efficiency than reordering during decoding. The experiments also show that the reordering at the chunk-level performs better than at the POS-level. | Chunk-Level Reordering of Source Language Sentences with Automatically Learned Rules for Statistical Machine Translation |
d252763280 | Supervised learning is a classic paradigm of relation extraction (RE). However, a well-performing model can still confidently make arbitrarily wrong predictions when exposed to samples of unseen relations. In this work, we propose a relation extraction method with rejection option to improve robustness to unseen relations. To enable the classifier to reject unseen relations, we introduce contrastive learning techniques and carefully design a set of class-preserving transformations to improve the discriminability between known and unseen relations. Based on the learned representation, inputs of unseen relations are assigned a low confidence score and rejected. Off-the-shelf open relation extraction (OpenRE) methods can be adopted to discover the potential relations in these rejected inputs. In addition, we find that the rejection can be further improved via readily available distantly supervised data. Experiments on two public datasets prove the effectiveness of our method capturing discriminative representations for unseen relation rejection. | Abstains from Prediction: Towards Robust Relation Extraction in Real World |
d27942273 | META-NET is a European network of excellence, founded in 2010, that consists of 60 research centres in 34 European countries. One of the key visions and goals of META-NET is a truly multilingual Europe, which is substantially supported and realised through language technologies. In this article we provide an overview of recent developments around the multilingual Europe topic, we also describe recent and upcoming events as well as recent and upcoming strategy papers. Furthermore, we provide overviews of two new emerging initiatives, the CEF.AT and ELRC activity on the one hand and the Cracking the Language Barrier federation on the other. The paper closes with several suggested next steps in order to address the current challenges and to open up new opportunities. | Fostering the Next Generation of European Language Technology: Recent Developments -Emerging Initiatives -Challenges and Opportunities |
d250390485 | In this work, cross-linguistic span prediction based on contextualized word embedding models is used together with neural machine translation (NMT) to transfer and apply the stateof-the-art models in natural language processing (NLP) to a low-resource language clinical corpus. Two directions are evaluated: (a) English models can be applied to translated texts to subsequently transfer the predicted annotations to the source language and (b) existing high-quality annotations can be transferred beyond translation and then used to train NLP models in the target language. Effectiveness and loss of transmission is evaluated using the German Berlin-Tübingen-Oncology Corpus (BRONCO) dataset with transferred external data from NCBI disease, SemEval-2013 drug-drug interaction (DDI) and i2b2/VA 2010 data. The use of English models for translated clinical texts has always involved attempts to take full advantage of the benefits associated with them (large pre-trained biomedical word embeddings). To improve advances in this area, we provide a general-purpose pipeline to transfer any annotated BRAT or CoNLL format to various target languages. For the entity class medication, good results were obtained with 0.806 F 1-score after re-alignment. Limited success occurred in the diagnosis and treatment class with results just below 0.5 F 1-score due to differences in annotation guidelines. | Cross-Language Transfer of High-Quality Annotations: Combining Neural Machine Translation with Cross-Linguistic Span Alignment to Apply NER to Clinical Texts in a Low-Resource Language |
d248095362 | Recent studies have found that removing the norm-bounded projection and increasing search steps in adversarial training can significantly improve robustness. However, we observe that a too large number of search steps can hurt accuracy. We aim to obtain strong robustness efficiently using fewer steps. Through a toy experiment, we find that perturbing the clean data to the decision boundary but not crossing it does not degrade the test accuracy. Inspired by this, we propose friendly adversarial data augmentation (FADA) to generate friendly adversarial data. On top of FADA, we propose geometry-aware adversarial training (GAT) to perform adversarial training on friendly adversarial data so that we can save a large number of search steps. Comprehensive experiments across two widely used datasets and three pretrained language models demonstrate that GAT can obtain stronger robustness via fewer steps. In addition, we provide extensive empirical results and in-depth analyses on robustness to facilitate future studies. | Improving Robustness of Language Models from a Geometry-aware Perspective |
d196206886 | State-of-the-art models for knowledge graph completion aim at learning a fixed embedding representation of entities in a multirelational graph which can generalize to infer unseen entity relationships at test time. This can be sub-optimal as it requires memorizing and generalizing to all possible entity relationships using these fixed representations. We thus propose a novel attentionbased method to learn query-dependent representation of entities which adaptively combines the relevant graph neighborhood of an entity leading to more accurate KG completion. The proposed method is evaluated on two benchmark datasets for knowledge graph completion, and experimental results show that the proposed model performs competitively or better than existing state-of-the-art, including recent methods for explicit multi-hop reasoning. Qualitative probing offers insight into how the model can reason about facts involving multiple hops in the knowledge graph, through the use of neighborhood attention. | A2N: Attending to Neighbors for Knowledge Graph Inference |
d890333 | We present a machine learning approach to evaluating the wellformedness of output of a machine translation system, using classifiers that learn to distinguish human reference translations from machine translations. This approach can be used to evaluate an MT system, tracking improvements over time; to aid in the kind of failure analysis that can help guide system development; and to select among alternative output strings. The method presented is fully automated and independent of source language, target language and domain. | A machine learning approach to the automatic evaluation of machine translation |
d570476 | We propose a structure called dependency forest for statistical machine translation. A dependency forest compactly represents multiple dependency trees. We develop new algorithms for extracting string-todependency rules and training dependency language models. Our forest-based string-to-dependency system obtains significant improvements ranging from 1.36 to 1.46 BLEU points over the tree-based baseline on the NIST | Dependency Forest for Statistical Machine Translation |
d237485150 | Existing black box search methods have achieved high success rate in generating adversarial attacks against NLP models. However, such search methods are inefficient as they do not consider the amount of queries required to generate adversarial attacks. Also, prior attacks do not maintain a consistent search space while comparing different search methods. In this paper, we propose a query efficient attack strategy to generate plausible adversarial examples on text classification and entailment tasks. Our attack jointly leverages attention mechanism and locality sensitive hashing (LSH) to reduce the query count. We demonstrate the efficacy of our approach by comparing our attack with four baselines across three different search spaces. Further, we benchmark our results across the same search space used in prior attacks. In comparison to attacks proposed, on an average, we are able to reduce the query count by 75% across all datasets and target models. We also demonstrate that our attack achieves a higher success rate when compared to prior attacks in a limited query setting. | A Strong Baseline for Query Efficient Attacks in a Black Box Setting |
d20339999 | Questions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and background of the person asking it, as well as the nature of their relation with the interlocutor. While the informational nature of questions has been extensively examined in the context of question-answering applications, their rhetorical aspects have been largely understudied.In this work we introduce an unsupervised methodology for extracting surface motifs that recur in questions, and for grouping them according to their latent rhetorical role. By applying this framework to the setting of question sessions in the UK parliament, we show that the resulting typology encodes key aspects of the political discourse-such as the bifurcation in questioning behavior between government and opposition parties-and reveals new insights into the effects of a legislator's tenure and political career ambitions. | Asking too much? The rhetorical role of questions in political discourse |
d255775289 | Utilizing citations for research artifacts (e.g., dataset, software) in scholarly papers contributes to efficient expansion of research artifact repositories and various applications e.g., the search, recommendation, and evaluation of such artifacts. This study focuses on citations using URLs (URL citations) and aims to identify and analyze research artifact citations automatically. This paper addresses the classification task for each URL citation to identify (1) the role that the referenced resources play in research activities, (2) the type of referenced resources, and (3) the reason why the author cited the resources. This paper proposes the classification method using section titles and footnote texts as new input features. We extracted URL citations from international conference papers as experimental data. | Classification of URL Citations in Scholarly Papers for Promoting Utilization of Research Artifacts |
d11060926 | Men are from Mars and women are from Venus -or so the genre of relationship literature would have us believe. But there is some truth in this idea, and researchers in fields as diverse as psychology, sociology, and linguistics have explored ways to better understand the differences between genders. In this paper, we take another look at the problem of gender discrimination and attempt to move beyond the typical surface-level text classification approach, by (1) identifying semantic and psycholinguistic word classes that reflect systematic differences between men and women and (2) finding differences between genders in the ways they use the same words. We describe several experiments and report results on a large collection of blogs authored by men and women.This work is licensed under a Creative Commons Attribution 4.0 International Licence.Licence details: | Zooming in on Gender Differences in Social Media |
d253107490 | We introduce a new open information extraction (OIE) benchmark for pre-trained language models (LM). Recent studies have demonstrated that pre-trained LMs, such as BERT and GPT, may store linguistic and relational knowledge. In particular, LMs are able to answer "fill-in-the-blank" questions when given a pre-defined relation category. Instead of focusing on pre-defined relations, we create an OIE benchmark aiming to fully examine the open relational information present in the pretrained LMs. We accomplish this by turning pre-trained LMs into zero-shot OIE systems. Surprisingly, pre-trained LMs are able to obtain competitive performance on both standard OIE datasets (CaRB and Re-OIE2016) and two new large-scale factual OIE datasets (TAC KBP-OIE and Wikidata-OIE) that we establish via distant supervision. For instance, the zeroshot pre-trained LMs outperform the F1 score of the state-of-the-art supervised OIE methods on our factual OIE datasets without needing to use any training sets. 1NP NP(a) Predicate extraction example. Dylan was in born Minnesota Key: Query: Dylan was born in Minnesota X X X X X 0.1 X X X X 0.2 0.1 X X X 0.05 0.05 0.3 X X | IELM: An Open Information Extraction Benchmark for Pre-Trained Language Models |
d10070164 | We present a cross-lingual method for determining NP structures. More specifically, we try to determine whether the semantics of tripartite noun compounds in context requires a left or right branching interpretation. The system exploits the difference in word position between languages as found in parallel corpora. We achieve a bracketing accuracy of 94.6%, significantly outperforming all systems in comparison and comparable to human performance. Our system generates large amounts of high-quality bracketed NPs in a multilingual context that can be used to train supervised learners. | From a Distance: Using Cross-lingual Word Alignments for Noun Compound Bracketing |
d253080689 | For text classification tasks, finetuned language models perform remarkably well. Yet, they tend to rely on spurious patterns in training data, thus limiting their performance on out-of-distribution (OOD) test data. Among recent models aiming to avoid this spurious pattern problem, adding extra counterfactual samples to the training data has proven to be very effective. Yet, counterfactual data generation is costly since it relies on human annotation. Thus, we propose a novel solution that only requires annotation of a small fraction (e.g., 1%) of the original training data, and uses automatic generation of extra counterfactuals in an encoding vector space. We demonstrate the effectiveness of our approach in sentiment classification, using IMDb data for training and other sets for OOD tests (i.e., Amazon, SemEval and Yelp). We achieve noticeable accuracy improvements by adding only 1% manual counterfactuals: +3% compared to adding +100% in-distribution training samples, +1.3% compared to alternate counterfactual approaches. | Robustifying Sentiment Classification by Maximally Exploiting Few Counterfactuals |
d253080692 | Many NLP datasets have been found to contain shortcuts: simple decision rules that achieve surprisingly high accuracy. However, it is difficult to discover shortcuts automatically. Prior work on automatic shortcut detection has focused on enumerating features like unigrams or bigrams, which can find only low-level shortcuts, or relied on post-hoc model interpretability methods like saliency maps, which reveal qualitative patterns without a clear statistical interpretation. In this work, we propose to use probabilistic grammars to characterize and discover shortcuts in NLP datasets. Specifically, we use a contextfree grammar to model patterns in sentence classification datasets and use a synchronous context-free grammar to model datasets involving sentence pairs. The resulting grammars reveal interesting shortcut features in a number of datasets, including both simple and high-level features, and automatically identify groups of test examples on which conventional classifiers fail. Finally, we show that the features we discover can be used to generate diagnostic contrast examples and incorporated into standard robust optimization methods to improve worst-group accuracy. 1 | Finding Dataset Shortcuts with Grammar Induction |
d252763332 | Recent advances in the field of abstractive summarization leverage pre-trained language models rather than train a model from scratch. However, such models are sluggish to train and accompanied by a massive overhead. Researchers have proposed a few lightweight alternatives such as smaller adapters to mitigate the drawbacks. Nonetheless, it remains uncertain whether using adapters benefits the task of summarization, in terms of improved efficiency without an unpleasant sacrifice in performance. In this work, we carry out multifaceted investigations on fine-tuning and adapters for summarization tasks with varying complexity: language, domain, and task transfer. In our experiments, fine-tuning a pre-trained language model generally attains a better performance than using adapters; the performance gap positively correlates with the amount of training data used. Notably, adapters exceed fine-tuning under extremely low-resource conditions. We further provide insights on multilinguality, model convergence, and robustness, hoping to shed light on the pragmatic choice of fine-tuning or adapters in abstractive summarization. | To Adapt or to Fine-tune: A Case Study on Abstractive Summarization |
d259370519 | This paper introduces the Unified Interactive Natural Understanding of the Italian Language (UINAUIL), a benchmark of six tasks for Italian Natural Language Understanding. We present a description of the tasks and software library that collects the data from the European Language Grid, harmonizes the data format, and exposes functionalities to facilitates data manipulation and the evaluation of custom models. We also present the results of tests conducted with available Italian and multilingual language models on UINAUIL, providing an updated picture of the current state of the art in Italian NLU. Video: https://www.youtube.com/watch? v=rZWKl9cPTbk | UINAUIL: A Unified Benchmark for Italian Natural Language Understanding |
d259376545 | In this paper, we present our submission to the IWSLT 2023 (Agarwal et al., 2023) Simultaneous Speech-to-Text Translation competition. Our participation involves three language directions: English-German, English-Chinese, and English-Japanese. Our proposed solution is a cascaded incremental decoding system that comprises an ASR model and an MT model. The ASR model is based on the U2++ architecture and can handle both streaming and offline speech scenarios with ease. Meanwhile, the MT model adopts the Deep-Transformer architecture. To improve performance, we explore methods to generate a confident partial target text output that guides the next MT incremental decoding process. In our experiments, we demonstrate that our simultaneous strategies achieve low latency while maintaining a loss of no more than 2 BLEU points when compared to offline systems. | The HW-TSC's Simultaneous Speech-to-Text Translation system for IWSLT 2023 evaluation |
d18819637 | We present ExB Themis -a word alignmentbased semantic textual similarity system developed for SemEval-2015 Task 2: Semantic Textual Similarity. It combines both string and semantic similarity measures as well as alignment features using Support Vector Regression. It occupies the first three places on Spanish data and additionally places second on English data. ExB Themis proved to be the best multilingual system among all participants. | ExB Themis: Extensive Feature Extraction from Word Alignments for Semantic Textual Similarity |
d44134226 | We explore story generation: creative systems that can build coherent and fluent passages of text about a topic. We collect a large dataset of 300K human-written stories paired with writing prompts from an online forum. Our dataset enables hierarchical story generation, where the model first generates a premise, and then transforms it into a passage of text. We gain further improvements with a novel form of model fusion that improves the relevance of the story to the prompt, and adding a new gated multi-scale self-attention mechanism to model long-range context. Experiments show large improvements over strong baselines on both automated and human evaluations. Human judges prefer stories generated by our approach to those from a strong non-hierarchical model by a factor of two to one. | Hierarchical Neural Story Generation |
d1306065 | The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline. | A Convolutional Neural Network for Modelling Sentences |
d11845088 | We present the implementation of a system which extracts not only lexicalized grammars but also feature-based lexicalized grammars from Korean Sejong Treebank. We report on some practical experiments where we extract TAG grammars and tree schemata. Above all, full-scale syntactic tags and well-formed morphological analysis in Sejong Treebank allow us to extract syntactic features. In addition, we modify Treebank for extracting lexicalized grammars and convert lexicalized grammars into tree schemata to resolve limited lexical coverage problem of extracted lexicalized grammars. | Extraction of Tree Adjoining Grammars from a Treebank for Korean |
d253237297 | Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG researches overlook how to utilize the attribute entanglement to enhance the diversity of the controlled generated texts. Facing this dilemma, we focus on a novel CTG scenario, i.e., blessing generation which is challenging because high-quality blessing texts require CTG models to comprehensively consider the entanglement between multiple attributes (e.g., objects and occasions). To promote the research on blessing generation, we present EBleT, a large-scale Entangled Blessing Text dataset containing 293K English sentences annotated with multiple attributes. Furthermore, we propose novel evaluation metrics to measure the quality of the blessing texts generated by the baseline models we designed. Our study opens a new research direction for controllable text generation and enables the development of attribute-entangled CTG models.Our dataset and source codes are available at https | Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation |
d233210669 | Semantic parsing using sequence-to-sequence models allows parsing of deeper representations compared to traditional word tagging based models.In spite of these advantages, widespread adoption of these models for real-time conversational use cases has been stymied by higher compute requirements and thus higher latency. In this work, we propose a non-autoregressive approach to predict semantic parse trees with an efficient seq2seq model architecture.By combining nonautoregressive prediction with convolutional neural networks, we achieve significant latency gains and parameter size reduction compared to traditional RNN models. Our novel architecture achieves up to an 81% reduction in latency on TOP dataset and retains competitive performance to non-pretrained models on three different semantic parsing datasets. Our code is available at https://github. com/facebookresearch/pytext. | Non-Autoregressive Semantic Parsing for Compositional Task-Oriented Dialog |
d11536389 | A vast majority of L1 vocabulary acquisition occurs through incidental learning during reading(Nation, 2001;Schmitt et al., 2001). We propose a probabilistic approach to generating code-mixed text as an L2 technique for increasing retention in adult lexical learning through reading. Our model that takes as input a bilingual dictionary and an English text, and generates a code-switched text that optimizes a defined "learnability" metric by constructing a factor graph over lexical mentions. Using an artificial language vocabulary, we evaluate a set of algorithms for generating code-switched text automatically by presenting it to Mechanical Turk subjects and measuring recall in a sentence completion task. | Generating Code-switched Text for Lexical Learning |
d218900601 | To advance understanding on how to engage readers, we advocate the novel task of automatic pull quote selection. Pull quotes are a component of articles specifically designed to catch the attention of readers with spans of text selected from the article and given more salient presentation. This task differs from related tasks such as summarization and clickbait identification by several aspects. We establish a spectrum of baseline approaches to the task, ranging from handcrafted features to a neural mixture-of-experts to cross-task models. By examining the contributions of individual features and embedding dimensions from these models, we uncover unexpected properties of pull quotes to help answer the important question of what engages readers. Human evaluation also supports the uniqueness of this task and the suitability of our selection models. The benefits of exploring this problem further are clear: pull quotes increase enjoyment and readability, shape reader perceptions, and facilitate learning. Code to reproduce this work is available at https://github.com/tannerbohn/AutomaticPullQuoteSelection. This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http://creative commons.org/licenses/by/4.0/. Dim 483 (65.4) | Catching Attention with Automatic Pull Quote Selection |
d249119312 | Current abstractive summarization systems tend to hallucinate content that is unfaithful to the source document, posing a risk of misinformation. To mitigate hallucination, we must teach the model to distinguish hallucinated summaries from faithful ones. However, the commonly used maximum likelihood training does not disentangle factual errors from other model errors. To address this issue, we propose a back-translation-style approach to augment negative samples that mimic factual errors made by the model. Specifically, we train an elaboration model that generates hallucinated documents given the reference summaries, and then generates negative summaries from the fake documents. We incorporate the negative samples into training through a controlled generator, which produces faithful/unfaithful summaries conditioned on the control codes. Additionally, we find that adding textual entailment data through multitasking further boosts the performance. Experiments on three datasets (XSum, GigaWord, and WikiHow) show that our method consistently improves faithfulness without sacrificing informativeness according to both human and automatic evaluation. 1 | Improving Faithfulness by Augmenting Negative Summaries from Fake Documents |
d226283967 | Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse. Meaning in DRT is modeled via a Discourse Representation Structure (DRS), a meaning representation with a model-theoretic interpretation, which is usually depicted as nested boxes. In contrast, a directed labeled graph is a common data structure used to encode semantics of natural language texts. The paper describes the procedure of dressing up DRSs as directed labeled graphs to include DRT as a new framework in the 2020 shared task on Cross-Framework and Cross-Lingual Meaning Representation Parsing. Since one of the goals of the shared task is to encourage unified models for several semantic graph frameworks, the conversion procedure was biased towards making the DRT graph framework somewhat similar to other graph-based meaning representation frameworks. * Part of the work was done while the author was at the University of Groningen.1 Throughout the paper, we mean a directed labeled graph when simply talking about graphs, unless stated otherwise. abstraction and serialization for cross-framework meaning representation parsing. | DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs |
d226284002 | Semantic role labeling (SRL) identifies predicate-argument structure(s) in a given sentence. Although different languages have different argument annotations, polyglot training, the idea of training one model on multiple languages, has previously been shown to outperform monolingual baselines, especially for low resource languages. In fact, even a simple combination of data has been shown to be effective with polyglot training by representing the distant vocabularies in a shared representation space. Meanwhile, despite the dissimilarity in argument annotations between languages, certain argument labels do share common semantic meaning across languages (e.g. adjuncts have more or less similar semantic meaning across languages). To leverage such similarity in annotation space across languages, we propose a method called Cross-Lingual Argument Regularizer (CLAR). CLAR identifies such linguistic annotation similarity across languages and exploits this information to map the target language arguments using a transformation of the space on which source language arguments lie. By doing so, our experimental results show that CLAR consistently improves SRL performance on multiple languages over monolingual and polyglot baselines for low resource languages. | CLAR: A Cross-Lingual Argument Regularizer for Semantic Role Labeling |
d231709707 | Despite the recent success of deep neural networks in natural language processing, the extent to which they can demonstrate human-like generalization capacities for natural language understanding remains unclear. We explore this issue in the domain of natural language inference (NLI), focusing on the transitivity of inference relations, a fundamental property for systematically drawing inferences. A model capturing transitivity can compose basic inference patterns and draw new inferences. We introduce an analysis method using synthetic and naturalistic NLI datasets involving clauseembedding verbs to evaluate whether models can perform transitivity inferences composed of veridical inferences and arbitrary inference types. We find that current NLI models do not perform consistently well on transitivity inference tasks, suggesting that they lack the generalization capacity for drawing composite inferences from provided training examples. The data and code for our analysis are publicly available at https://github.com/ verypluming/transitivity. | Exploring Transitivity in Neural NLI Models through Veridicality |
d237940453 | We study the problem of coarse-grained response selection in retrieval-based dialogue systems. The problem is equally important with fine-grained response selection, but is less explored in existing literature. In this paper, we propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations. In our CFC model, dense representations of query, candidate contexts and responses is learned based on the multi-tower architecture using contextual matching, and richer knowledge learned from the one-tower architecture (fine-grained) is distilled into the multi-tower architecture (coarse-grained) to enhance the performance of the retriever. To evaluate the performance of the proposed model, we construct two new datasets based on the Reddit comments dump and Twitter corpus. Extensive experimental results on the two datasets show that the proposed method achieves huge improvement over all evaluation metrics compared with traditional baseline methods. | Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations |
d1683643 | A standard form of analysis for linguistic typology is the universal implication. These implications state facts about the range of extant languages, such as "if objects come after verbs, then adjectives come after nouns." Such implications are typically discovered by painstaking hand analysis over a small sample of languages. We propose a computational model for assisting at this process. Our model is able to discover both well-known implications as well as some novel implications that deserve further study. Moreover, through a careful application of hierarchical analysis, we are able to cope with the well-known sampling problem: languages are not independent. | A Bayesian Model for Discovering Typological Implications |
d247594288 | Transformer-based language models usually treat texts as linear sequences. However, most texts also have an inherent hierarchical structure, i. e., parts of a text can be identified using their position in this hierarchy. In addition, section titles usually indicate the common topic of their respective sentences. We propose a novel approach to formulate, extract, encode and inject hierarchical structure information explicitly into an extractive summarization model based on a pre-trained, encoderonly Transformer language model (HiStruct+ model), which improves SOTA ROUGEs for extractive summarization on PubMed and arXiv substantially. Using various experimental settings on three datasets (i. e., CNN/DailyMail, PubMed and arXiv), our HiStruct+ model outperforms a strong baseline collectively, which differs from our model only in that the hierarchical structure information is not injected. It is also observed that the more conspicuous hierarchical structure the dataset has, the larger improvements our method gains. The ablation study demonstrates that the hierarchical position information is the main contributor to our model's SOTA performance. | HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Information |
d4953145 | Previously, neural methods in grammatical error correction (GEC) did not reach state-ofthe-art results compared to phrase-based statistical machine translation (SMT) baselines. We demonstrate parallels between neural GEC and low-resource neural MT and successfully adapt several methods from low-resource MT to neural GEC. We further establish guidelines for trustable results in neural GEC and propose a set of model-independent methods for neural GEC that can be easily applied in most GEC settings. Proposed methods include adding source-side noise, domain-adaptation techniques, a GEC-specific training-objective, transfer learning with monolingual data, and ensembling of independently trained GEC models and language models. The combined effects of these methods result in better than state-of-the-art neural GEC models that outperform previously best neural GEC systems by more than 10% M 2 on the CoNLL-2014 benchmark and 5.9% on the JFLEG test set. Non-neural state-of-the-art systems are outperformed by more than 2% on the CoNLL-2014 benchmark and by 4% on JFLEG. | Approaching Neural Grammatical Error Correction as a Low-Resource Machine Translation Task |
d53083307 | We study the automatic generation of syntactic paraphrases using four different models for generation: data-to-text generation, textto-text generation, text reduction and text expansion, We derive training data for each of these tasks from the WebNLG dataset and we show (i) that conditioning generation on syntactic constraints effectively permits the generation of syntactically distinct paraphrases for the same input and (ii) that exploiting different types of input (data, text or data+text) further increases the number of distinct paraphrases that can be generated for a given input. | Generating Syntactic Paraphrases |
d15623991 | In this paper, we propose a machine learning approach to rhetorical role identification from legal documents. In our approach, we annotate roles in sample documents with the help of legal experts and take them as training data. Conditional random field model has been trained with the data to perform rhetorical role identification with reinforcement of rich feature sets. The understanding of structure of a legal document and the application of mathematical model can brings out an effective summary in the final stage. Other important new findings in this work include that the training of a model for one sub-domain can be extended to another sub-domains with very limited augmentation of feature sets. Moreover, we can significantly improve extraction-based summarization results by modifying the ranking of sentences with the importance of specific roles. | Automatic Identification of Rhetorical Roles using Conditional Random Fields for Legal Document Summarization |
d252819465 | In the effort to minimize the risk of extinction of a language, linguistic resources are fundamental. Quechua, a low-resource language from South America, is a language spoken by millions but, despite several efforts in the past, still lacks the resources necessary to build high-performance computational systems. In this article, we present WordNet-QU which signifies the inclusion of Quechua in a wellknown lexical database called wordnet. We propose WordNet-QU to be included as an extension to wordnet after demonstrating a manually-curated collection of multiple digital resources for lexical use in Quechua. Our work uses the synset alignment algorithm to compare Quechua to its geographically nearest high-resource language, Spanish. Altogether, we propose a total of 28,582 unique synset IDs divided according to region like so: 20510 for Southern Quechua, 5993 for Central Quechua, 1121 for Northern Quechua, and 958 for Amazonian Quechua. | WordNet-QU: Development of a Lexical Database for Quechua Varieties |
d365363 | Targeted paraphrasing is a new approach to the problem of obtaining cost-effective, reasonable quality translation that makes use of simple and inexpensive human computations by monolingual speakers in combination with machine translation. The key insight behind the process is that it is possible to spot likely translation errors with only monolingual knowledge of the target language, and it is possible to generate alternative ways to say the same thing (i.e. paraphrases) with only monolingual knowledge of the source language. Evaluations demonstrate that this approach can yield substantial improvements in translation quality. | Improving Translation via Targeted Paraphrasing |
d160187 | Previous approaches to pronominalization have largely been theoretical rather than applied in nature. Frequently, such methods are based on Centering Theory, which deals with the resolution of anaphoric pronouns. But it is not clear that complex theoretical mechanisms, while having satisfying explanatory power, are necessary for the actual generation of pronouns. We first illustrate examples of pronouns from various domains, describe a simple method for generating pronouns in an implemented multi-page generation system, and present an evaluation of its performance. | Pronominalization in Generated Discourse and Dialogue |
d11254817 | Traditional name transliteration methods largely ignore source context information and inter-dependency among entities for entity disambiguation. We propose a novel approach to leverage state-of-the-art Entity Linking (EL) techniques to automatically correct name transliteration results, using collective inference from source contexts and additional evidence from knowledge base. Experiments on transliterating names from seven languages to English demonstrate that our approach achieves 2.6% to 15.7% absolute gain over the baseline model, and significantly advances state-of-the-art. When contextual information exists, our approach can achieve further gains (24.2%) by collectively transliterating and disambiguating multiple related entities. We also prove that combining Entity Linking and projecting resources from related languages obtained comparable performance as the method using the same amount of training pairs in the original languages without Entity Linking. 1 2 | Leveraging Entity Linking and Related Language Projection to Improve Name Transliteration |
d174800279 | Learning to hash via generative models has become a powerful paradigm for fast similarity search in documents retrieval. To get binary representation (i.e., hash codes), the discrete distribution prior (i.e., Bernoulli Distribution) is applied to train the variational autoencoder (VAE). However, the discrete stochastic layer is usually incompatible with the backpropagation in the training stage and thus causes a gradient flow problem. In this paper, we propose a method, Doc2hash, that solves the gradient flow problem of the discrete stochastic layer by using continuous relaxation on priors, and trains the generative model in an end-to-end manner to generate hash codes. In qualitative and quantitative experiments, we show the proposed model outperforms other state of the art methods. | Doc2hash: Learning Discrete Latent Variables for Document Retrieval |
d258564506 | This paper aims to benchmark recent progress in language understanding models that output contextualised representations at the character level. Many such modelling architectures and methods to train those architectures have been proposed, but it is currently unclear what the relative contributions of the architecture vs. the pretraining objective are to final model performance. We explore the design space of such models, comparing architectural innovations (Clark et al., 2022; Jaegle et al., 2022;Tay et al., 2021), and a variety of different pretraining objectives on a suite of evaluation tasks in order to find the optimal way to build and train character-level BERT-like models. We find that the best recipe combines the Charformer and CANINE model architectures, and follows the CANINE training procedure. This model exceeds the performance of a tokenbased model trained with the same settings on the same data, suggesting that character-level models are ready for more widespread adoption. Unfortunately, the best method to train character-level models still relies on a learnt tokeniser during pretraining, and final model performance is highly dependent on tokeniser quality. We believe our results demonstrate the readiness of character-level models for multilingual language representation, and encourage NLP practitioners to try them for their needs. | What is the best recipe for character-level encoder-only modelling? |
d237507023 | Table fillingbased relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from complex sentences. However, this kind of methods are far from their full potential because most of them only focus on using local features but ignore the global associations of relations and of token pairs, which increases the possibility of overlooking some important information during triple extraction. To overcome this deficiency, we propose a global feature-oriented triple extraction model that makes full use of the mentioned two kinds of global associations. Specifically, we first generate a table feature for each relation. Then two kinds of global associations are mined from the generated table features. Next, the mined global associations are integrated into the table feature of each relation. This "generate-mine-integrate" process is performed multiple times so that the table feature of each relation is refined step by step. Finally, each relation's table is filled based on its refined table feature, and all triples linked to this relation are extracted based on its filled table. We evaluate the proposed model on three benchmark datasets. Experimental results show our model is effective and it achieves state-of-the-art results on all of these datasets. The source code of our work is available at: https://github.com/neukg/GRTE. † Both authors contribute equally to this work and share co-first authorship. | A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling |
d234469811 | We propose a cascade of neural models that performs sentence classification, phrase recognition, and triple extraction to automatically structure the scholarly contributions of NLP publications in English. To identify the most important contribution sentences in a paper, we used a BERT-based classifier with positional features (Subtask 1). A BERT-CRF model was used to recognize and characterize relevant phrases in contribution sentences (Subtask 2). We categorized the triples into several types based on whether and how their elements were expressed in text, and addressed each type using separate BERT-based classifiers as well as rules (Subtask 3). Our system was officially ranked second in Phase 1 evaluation and first in both parts of Phase 2 evaluation. After fixing a submission error in Phase 1, our approach yielded the best results overall. In this paper, in addition to a system description, we also provide further analysis of our results, highlighting its strengths and limitations. We make our code publicly available at https://github.com/ Liu-Hy/nlp-contrib-graph. | UIUC BioNLP at SemEval-2021 Task 11: A Cascade of Neural Models for Structuring Scholarly NLP Contributions |
d260063228 | Negation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous researches, state-of-the-art methods performed better without the explicit use of syntactic structure. This work revisits the syntax-based approach and re-evaluates the effectiveness of syntactic structure in negation scope resolution. We replace the parser utilized in the prior works with state-of-the-art parsers and modify the syntax-based heuristic rules. The experimental results demonstrate that the simple modifications enhance the performance of the prior syntax-based method to the same level as state-of-the-art end-to-end neural-based methods. | Revisiting Syntax-Based Approach in Negation Scope Resolution |
d1875219 | The ever growing amount of web images and their associated texts offers new opportunities for integrative models bridging natural language processing and computer vision. However, the potential benefits of such data are yet to be fully realized due to the complexity and noise in the alignment between image content and text. We address this challenge with contributions in two folds: first, we introduce the new task of image caption generalization, formulated as visually-guided sentence compression, and present an efficient algorithm based on dynamic beam search with dependency-based constraints. Second, we release a new large-scale corpus with 1 million image-caption pairs achieving tighter content alignment between images and text. Evaluation results show the intrinsic quality of the generalized captions and the extrinsic utility of the new imagetext parallel corpus with respect to a concrete application of image caption transfer. | Generalizing Image Captions for Image-Text Parallel Corpus |
d17352617 | In order to obtain a fine-grained evaluation of parser accuracy over naturally occurring text, we study 100 examples each of ten reasonably frequent linguistic phenomena, randomly selected from a parsed version of the English Wikipedia. We construct a corresponding set of gold-standard target dependencies for these 1000 sentences, operationalize mappings to these targets from seven state-of-theart parsers, and evaluate the parsers against this data to measure their level of success in identifying these dependencies. | Parser Evaluation over Local and Non-Local Deep Dependencies in a Large Corpus |
d14898349 | This article describes our novel approach to the automated detection and analysis of metaphors in text. We employ robust, quantitative language processing to implement a system prototype combined with sound social science methods for validation. We show results in 4 different languages and discuss how our methods are a significant step forward from previously established techniques of metaphor identification. We use Topical Structure and Tracking, an Imageability score, and innovative methods to build an effective metaphor identification system that is fully automated and performs well over baseline. | Robust Extraction of Metaphors from Novel Data |
d233024820 | In this work, we present our approach for solving the SemEval 2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation (MCL-WiC). The task is a sentence pair classification problem where the goal is to detect whether a given word common to both the sentences evokes the same meaning. We submit systems for both the settings -Multilingual (the pair's sentences belong to the same language) and Cross-Lingual (the pair's sentences belong to different languages). The training data is provided only in English. Consequently, we employ cross-lingual transfer techniques. Our approach employs finetuning pre-trained transformer-based language models, like ELECTRA and ALBERT, for the English task and XLM-R for all other tasks. To improve these systems' performance, we propose adding a signal to the word to be disambiguated and augmenting our data by sentence pair reversal. We further augment the dataset provided to us with WiC, XL-WiC and SemCor 3.0. Using ensembles, we achieve strong performance in the Multilingual task, placing first in the EN-EN and FR-FR subtasks. For the Cross-Lingual setting, we employed translate-test methods and a zero-shot method, using our multilingual models, with the latter performing slightly better. | MCL@IITK at SemEval-2021 Task 2: Multilingual and Cross-lingual Word-in-Context Disambiguation using Augmented Data, Signals, and Transformers |
d233024836 | In this work, we present our approach and findings for SemEval-2021 Task 5 -Toxic Spans Detection. The task's main aim was to identify spans to which a given text's toxicity could be attributed. The task is challenging mainly due to two constraints: the small training dataset and imbalanced class distribution. Our paper investigates two techniques, semi-supervised learning and learning with Self-Adjusting Dice Loss, for tackling these challenges. Our submitted system (ranked ninth on the leader board) consisted of an ensemble of various pre-trained Transformer Language Models trained using either of the above-proposed techniques. | IITK@Detox at SemEval-2021 Task 5: Semi-Supervised Learning and Dice Loss for Toxic Spans Detection |
d235166504 | Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most of current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from datarich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios. | OntoED: Low-resource Event Detection with Ontology Embedding |
d14151217 | This paper investigates how linguistic knowledge mined from large text corpora can aid the generation of natural language descriptions of videos. Specifically, we integrate both a neural language model and distributional semantics trained on large text corpora into a recent LSTM-based architecture for video description. We evaluate our approach on a collection of Youtube videos as well as two large movie description datasets showing significant improvements in grammaticality while modestly improving descriptive quality. | Improving LSTM-based Video Description with Linguistic Knowledge Mined from Text |
d34032948 | Reasoning and inference are central to human and artificial intelligence. Modeling inference in human language is very challenging. With the availability of large annotated data(Bowman et al., 2015), it has recently become feasible to train neural network based inference models, which have shown to be very effective. In this paper, we present a new state-of-the-art result, achieving the accuracy of 88.6% on the Stanford natural language inference dataset. Unlike the previous top models that use very complicated network architectures, we first demonstrate that carefully designing sequential inference models based on chain LSTMs can outperform all previous models. Based on this, we further show that by explicitly considering recursive architectures in both local inference modeling and inference composition, we achieve additional improvement. Particularly, incorporating syntactic parsing information contributes to our best result-it further improves the performance even when added to the already very strong model. | Enhancing and Combining Sequential and Tree LSTM for Natural Language Inference |
d259376569 | In this paper, we introduce a fine-tuned transformer-based model focused on problematic webpage classification to identify webpages promoting hate and violence of various forms. Due to the unavailability of labelled problematic webpage data, first we propose a novel webpage data collection strategy which leverages well-studied short-text hate speech datasets. We have introduced a custom GPT-4 few-shot prompt annotation scheme taking various webpage features to label the prohibitively expensive webpage annotation task. The resulting annotated data is used to build our problematic webpage classification model. We report the accuracy (87.6% F1-score) of our webpage classification model and conduct a detailed comparison of it against other state-of-the-art hate speech classification model on problematic webpage identification task. Finally, we have showcased the importance of various webpage features in identifying a problematic webpage. | |
d258463927 | The role of pedagogical code-switching (henceforth CS) has been an arising topic of inquiry across the globe; hence, the global surge in bilingualism significantly opened an opportunity to use CS as a resource in class sessions for effective learning. This study aims to distinguish the factors that influence the attitudes of the participants toward Tagalog and English pedagogical CS and identify the significant differences between English, Tagalog, and CS among Filipinos. Anchored on Myers-Scotton's (1993) Markedness model framework, this quasi-experimental study aims to identify the attitudes towards pedagogical CS compared to monolingual English and monolingual Tagalog. To do this, the researchers used the verbal Guise technique (VGT), an innovative approach used to study attitudes, which is composed of three speakers for each language (English, Tagalog, and CS), and integrated it within a google forms questionnaire that has a 4-point Likert Scale adapted from the study of Valerio(2015), which were then given and listened to the 784 purposively sampled senior high school and college students within the different universities in the Philippines. The researchers then analyzed the data using a non-parametric statistical treatment known as Friedman's ANOVA and Kendall coefficient of concordance, which compares three groups without the independent-dependent variable relationships. | Attitudes towards pedagogical code-switching: A verbal guise approach |
d259370869 | The past decade has observed significant attention toward developing computational methods for classifying social media data based on the presence or absence of mental health conditions. In the context of mental health, for clinicians to make an accurate diagnosis or provide personalized intervention, it is crucial to identify fine-grained mental health symptoms. To this end, we conduct a focused study on depression disorder and introduce a new task of identifying fine-grained depressive symptoms from memes. Toward this, we create a highquality dataset (RESTORE) annotated with 8 fine-grained depression symptoms based on the clinically adopted PHQ-9 questionnaire. We benchmark RESTORE on 20 strong monomodal and multimodal methods. Additionally, we show how imposing orthogonal constraints on textual and visual feature representations in a multimodal setting can enforce the model to learn non-redundant and de-correlated features leading to a better prediction of fine-grained depression symptoms. Further, we conduct an extensive human analysis and elaborate on the limitations of existing multimodal models that often overlook the implicit connection between visual and textual elements of a meme. . 2014. Nonverbal social withdrawal in depression: Evidence from manual and automatic analyses. Image and vision computing, 32(10):641-647. . 2015. Topic model for identifying suicidal ideation in chinese microblog. | Towards Identifying Fine-Grained Depression Symptoms from Memes |
d4391686 | Differentiating intrinsic language words from transliterable words is a key step aiding text processing tasks involving different natural languages. We consider the problem of unsupervised separation of transliterable words from native words for text in Malayalam language. Outlining a key observation on the diversity of characters beyond the word stem, we develop an optimization method to score words based on their nativeness. Our method relies on the usage of probability distributions over character n-grams that are refined in step with the nativeness scorings in an iterative optimization formulation. Using an empirical evaluation, we illustrate that our method, DTIM, provides significant improvements in nativeness scoring for Malayalam, establishing DTIM as the preferred method for the task. | Unsupervised Separation of Transliterable and Native Words for Malayalam |
d11267601 | We develop a high-quality multi-turn dialog dataset, DailyDialog, which is intriguing in several aspects. The language is human-written and less noisy. The dialogues in the dataset reflect our daily communication way and cover various topics about our daily life. We also manually label the developed dataset with communication intention and emotion information. Then, we evaluate existing approaches on DailyDialog dataset and hope it benefit the research field of dialog systems 1 . | DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset |
d248178003 | We introduce ChemDisGene, a new dataset for training and evaluating multi-class multi-label document-level biomedical relation extraction models. Our dataset contains 80k biomedical research abstracts labeled with mentions of chemicals, diseases, and genes, portions of which human experts labeled with 18 types of biomedical relationships between these entities (intended for evaluation), and the remainder of which (intended for training) has been distantly labeled via the CTD database with approximately 78% accuracy. In comparison to similar preexisting datasets, ours is both substantially larger and cleaner; it also includes annotations linking mentions to their entities. We also provide three baseline deep neural network relation extraction models trained and evaluated on our new dataset. | A Distant Supervision Corpus for Extracting Biomedical Relationships Between Chemicals, Diseases and Genes |
d674402 | This paper presents a simple yet in practice very efficient technique serving for automatic detection of those positions in a partof-speech tagged corpus where an error is to be suspected. The approach is based on the idea of learning and later application of "negative bigrams", i.e. on the search for pairs of adjacent tags which constitute an incorrect configuration in a text of a particular language (in English, e.g., the bigram ARTICLE -FINITE VERB). Further, the paper describes the generalization of the "negative bigrams" into "negative n-grams", for any natural n, which indeed provides a powerful tool for error detection in a corpus. The implementation is also discussed, as well as evaluation of results of the approach when used for error detection in the NEGRA® corpus of German, and the general implications for the quality of results of statistical taggers. Illustrative examples in the text are taken from German, and hence at least a basic command of this language would be helpful for their understanding -due to the complexity of the necessary accompanying explanation, the examples are neither glossed nor translated. However, the central ideas of the paper should be understandable also without any knowledge of German. | (Semi-)Automatic Detection of Errors in PoS-Tagged Corpora |
d25111673 | The problem of AMR-to-text generation is to recover a text representing the same meaning as an input AMR graph. The current state-of-the-art method uses a sequence-to-sequence model, leveraging LSTM for encoding a linearized AMR structure. Although it is able to model non-local semantic information, a sequence LSTM can lose information from the AMR graph structure, and thus faces challenges with large graphs, which result in long sequences. We introduce a neural graph-to-sequence model, using a novel LSTM structure for directly encoding graph-level semantics. On a standard benchmark, our model shows superior results to existing methods in the literature. | A Graph-to-Sequence Model for AMR-to-Text Generation |
d249062562 | We argue that disentangling content selection from the budget used to cover salient content improves the performance and applicability of abstractive summarizers. Our method, FAC-TORSUM 1 , does this disentanglement by factorizing summarization into two steps through an energy function: (1) generation of abstractive summary views covering salient information in subsets of the input document (document views) ; (2) combination of these views into a final summary, following a budget and content guidance. This guidance may come from different sources, including from an advisor model such as BART or BigBird, or in oracle modefrom the reference. This factorization achieves significantly higher ROUGE scores on multiple benchmarks for long document summarization, namely PubMed, arXiv, and GovReport. Notably, our model is effective for domain adaptation. When trained only on PubMed, it achieves a 46.29 ROUGE-1 score on arXiv, outperforming PEGASUS trained in domain by a large margin. Our experimental results indicate that the performance gains are due to more flexible budget adaptation and processing of shorter contexts provided by partial document views. | Factorizing Content and Budget Decisions in Abstractive Summarization of Long Documents |
d227905056 | In linguistics and cognitive science, Logical metonymies are defined as type clashes between an event-selecting verb and an entitydenoting noun (e.g. The editor finished the article), which are typically interpreted by inferring a hidden event (e.g. reading) on the basis of contextual cues. This paper tackles the problem of logical metonymy interpretation, that is, the retrieval of the covert event via computational methods. We compare different types of models, including the probabilistic and the distributional ones previously introduced in the literature on the topic. For the first time, we also tested on this task some of the recent Transformer-based models, such as BERT, RoBERTa, XLNet, and GPT-2.Our results show a complex scenario, in which the best Transformer-based models and some traditional distributional models perform very similarly. However, the low performance on some of the testing datasets suggests that logical metonymy is still a challenging phenomenon for computational modeling. | Comparing Probabilistic, Distributional and Transformer-Based Models on Logical Metonymy Interpretation |
d239768404 | We present the task of Automated Punishment Extraction (APE) in sentencing decisions from criminal court cases in Hebrew. Addressing APE will enable the identification of sentenc ing patterns and constitute an important step ping stone for many follow up legal NLP ap plications in Hebrew, including the prediction of sentencing decisions. We curate a dataset of sexual assault sentencing decisions and a manuallyannotated evaluation dataset, and implement rulebased and supervised models. We find that while supervised models can iden tify the sentence containing the punishment with good accuracy, rulebased approaches outperform them on the full APE task. We con clude by presenting a first analysis of sentenc ing patterns in our dataset and analyze com mon models' errors, indicating avenues for fu ture work, such as distinguishing between pro bation and actual imprisonment punishment. We will make all our resources available upon request, including data, annotation, and first benchmark models. | Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language |
d126182481 | Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11x empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU. | Query-focused Sentence Compression in Linear Time |
d222341819 | Semi-supervision is a promising paradigm for Bilingual Lexicon Induction (BLI) with limited annotations. However, previous semisupervised methods do not fully utilize the knowledge hidden in annotated and nonannotated data, which hinders further improvement of their performance. In this paper, we propose a new semi-supervised BLI framework to encourage the interaction between the supervised signal and unsupervised alignment. We design two message-passing mechanisms to transfer knowledge between annotated and non-annotated data, named prior optimal transport and bi-directional lexicon update respectively. Then, we perform semi-supervised learning based on a cyclic or a parallel parameter feeding routine to update our models. Our framework is a general framework that can incorporate any supervised and unsupervised BLI methods based on optimal transport. Experimental results on MUSE and VecMap datasets show significant improvement of our models. Ablation study also proves that the two-way interaction between the supervised signal and unsupervised alignment accounts for the gain of the overall performance. Results on distant language pairs further illustrate the advantage and robustness of our proposed method. | Semi-Supervised Bilingual Lexicon Induction with Two-way Interaction |
d4559639 | To provide better access of the inventory to buyers and better search engine optimization, e-Commerce websites are automatically generating millions of easily searchable browse pages. A browse page consists of a set of slot name/value pairs within a given category, grouping multiple items which share some characteristics. These browse pages require a title describing the content of the page. Since the number of browse pages are huge, manual creation of these titles is infeasible. Previous statistical and neural approaches depend heavily on the availability of large amounts of data in a language. In this research, we apply sequence-to-sequence models to generate titles for high-& low-resourced languages by leveraging transfer learning. We train these models on multi-lingual data, thereby creating one joint model which can generate titles in various different languages. Performance of the title generation system is evaluated on three different languages; English, German, and French, with a particular focus on lowresourced French language. | Multi-lingual neural title generation for e-Commerce browse pages |
d17814199 | The challenge of making cost-effective implementations of auditory models has led us to pursue an analog VLSI micro-power approach. Experiments with the first few generations of analog cochlea chips showed some of both the potential and the problems of this approach. The inherent exponential behavior of MOS transistors in the subthreshold or weak-inversion region leads to nonlinear filter circuits, in which the small-signal and large-signal behaviors can be quite different. Early problems with instability, poor dynamic range, and excessive noise are now understood in terms of the transition behavior between these regions, and this understanding has led us to design filter stages with appropriately compressive behavior, resulting in more robust cochea performance. Several types of correlator circuits to follow the cochlea have also been developed into working demonstrations. Videotapes of circuit outputs and simulations illustrate the recent ideas and progress. | ANALOG IMPLEMENTATIONS OF AUDITORY MODELS |
d32020944 | This article is about annotating clauses with nonverbal predication in version 2 of Estonian UD treebank. Three possible annotation schemas are discussed, among which separating existential clauses from copular clauses would be theoretically most sound but would need too much manual labor and could possibly yield inconcistent annotation. Therefore, a solution has been adapted which separates existential clauses consisting only of subject and (copular) verb olema be from all other olema-clauses. | Estonian copular and existential constructions as an UD annotation problem |
d12607082 | In this paper, we propose new methods to learn Chinese word representations. Chinese characters are composed of graphical components, which carry rich semantics. It is common for a Chinese learner to comprehend the meaning of a word from these graphical components. As a result, we propose models that enhance word representations by character glyphs. The character glyph features are directly learned from the bitmaps of characters by convolutional auto-encoder(convAE), and the glyph features improve Chinese word representations which are already enhanced by character embeddings. Another contribution in this paper is that we created several evaluation datasets in traditional Chinese and made them public. | Learning Chinese Word Representations From Glyphs Of Characters |
d9497992 | We describe two approaches to analyzing and tagging team discourse using Latent Semantic Analysis (LSA) to predict team performance. The first approach automatically categorizes the contents of each statement made by each of the three team members using an established set of tags. Performance predicting the tags automatically was 15% below human agreement. These tagged statements are then used to predict team performance. The second approach measures the semantic content of the dialogue of the team as a whole and accurately predicts the team's performance on a simulated military mission. | Automated Team Discourse Annotation and Performance Prediction Using LSA |
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