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d229923411 | Conversational Machine Reading (CMR) aims at answering questions in complicated interactive scenarios. Machine needs to answer questions through interactions with users based on given rule document, user scenario and dialogue history, and even initiatively asks questions for clarification if necessary. Namely, the answer to the task needs a machine in the response of either Yes, No, Irrelevant or to raise a follow-up question for further clarification. To effectively capture multiple objects in such a challenging task, graph modeling is supposed to be adopted, though it is surprising that this does not happen until this work proposes a dialogue graph modeling framework by incorporating two complementary graph models, i.e., explicit discourse graph and implicit discourse graph, which respectively capture explicit and implicit interactions hidden in the rule documents. The proposed model is evaluated on the ShARC benchmark and achieves new state-of-the-art by first exceeding the milestone accuracy score of 80%. The source code of our paper is available at https: //github.com/ozyyshr/DGM * Equal contribution. †Corresponding author. | Dialogue Graph Modeling for Conversational Machine Reading |
d236459832 | Multimodal pre-training models, such as LXMERT, have achieved excellent results in downstream tasks. However, current pretrained models require large amounts of training data and have huge model sizes, which make them difficult to apply in low-resource situations. How to obtain similar or even better performance than a larger model under the premise of less pre-training data and smaller model size has become an important problem. In this paper, we propose a new Multi-stage Pre-training (MSP) method, which uses information at different granularities from word, phrase to sentence in both texts and images to pre-train the model in stages. We also design several different pre-training tasks suitable for the information granularity in different stage in order to efficiently capture the diverse knowledge from a limited corpus. We take a Simplified LXMERT (LXMERT-S), which has only 45.9% parameters of the original LXMERT model and 11.76% of the original pre-training data as the testbed of our MSP method. Experimental results show that our method achieves comparable performance to the original LXMERT model in all downstream tasks, and even outperforms the original model in Image-Text Retrieval task. | Multi-stage Pre-training over Simplified Multimodal Pre-training Models |
d115213545 | Fluid Construction Grammar (FCG) is a new linguistic formalism designed to explore in how far a construction grammar approach can be used for handling open-ended grounded dialogue, i.e. dialogue between or with autonomous embodied agents about the world as experienced through their sensory-motor apparatus. We seek scalable, open-ended language systems by giving agents both the ability to use existing conventions or ontologies, and to invent or learn new ones as the needs arise. This paper contains a brief introduction to the key ideas behind FCG and its current status. | A (very) Brief Introduction to Fluid Construction Grammar |
d196201415 | In summarization, automatic evaluation metrics are usually compared based on their ability to correlate with human judgments. Unfortunately, the few existing human judgment datasets have been created as by-products of the manual evaluations performed during the DUC/TAC shared tasks. However, modern systems are typically better than the best systems submitted at the time of these shared tasks. We show that, surprisingly, evaluation metrics which behave similarly on these datasets (average-scoring range) strongly disagree in the higher-scoring range in which current systems now operate. It is problematic because metrics disagree yet we can't decide which one to trust. This is a call for collecting human judgments for high-scoring summaries as this would resolve the debate over which metrics to trust. This would also be greatly beneficial to further improve summarization systems and metrics alike. | Studying Summarization Evaluation Metrics in the Appropriate Scoring Range |
d237941033 | Paraphrase generation is a longstanding NLP task that has diverse applications for downstream NLP tasks. However, the effectiveness of existing efforts predominantly relies on large amounts of golden labeled data. Though unsupervised endeavors have been proposed to address this issue, they may fail to generate meaningful paraphrases due to the lack of supervision signals. In this work, we go beyond the existing paradigms and propose a novel approach to generate high-quality paraphrases with weak supervision data. Specifically, we tackle the weakly-supervised paraphrase generation problem by: (1) obtaining abundant weakly-labeled parallel sentences via retrievalbased pseudo paraphrase expansion; and (2) developing a meta-learning framework to progressively select valuable samples for finetuning a pre-trained language model, i.e., BART, on the sentential paraphrasing task. We demonstrate that our approach achieves significant improvements over existing unsupervised approaches, and is even comparable in performance with supervised state-of-the-arts. | Learning to Selectively Learn for Weakly-supervised Paraphrase Generation |
d237485553 | We present models which complete missing text given transliterations of ancient Mesopotamian documents, originally written on cuneiform clay tablets (2500 BCE -100 CE). Due to the tablets' deterioration, scholars often rely on contextual cues to manually fill in missing parts in the text in a subjective and time-consuming process. We identify that this challenge can be formulated as a masked language modelling task, used mostly as a pretraining objective for contextualized language models. Following, we develop several architectures focusing on the Akkadian language, the lingua franca of the time. We find that despite data scarcity (1M tokens) we can achieve state of the art performance on missing tokens prediction (89% hit@5) using a greedy decoding scheme and pretraining on data from other languages and different time periods. Finally, we conduct human evaluations showing the applicability of our models in assisting experts to transcribe texts in extinct languages. | Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach |
d256183877 | The availability of personal writings in electronic format provides researchers in the fields of linguistics, psychology, and computational linguistics with an unprecedented chance to study, on a large scale, the relationship between language use and the demographic background of writers, allowing us to better understand people across different demographics. In this article, we analyze the relation between language and demographics by developing cross-demographic word models to identify words with usage bias, or words that are used in significantly different ways by speakers of different demographics. Focusing on three demographic categories, namely, location, gender, and industry, we identify words with significant usage differences in each category and investigate various approaches of encoding a word's usage, allowing us to identify language aspects that contribute to the differences. Our word models using topic-based features achieve at least 20% improvement in accuracy over the baseline for all demographic categories, even for scenarios with classification into 15 categories, illustrating the usefulness of topic-based features in identifying word usage differences. Further, we note that for location and industry, topics extracted from immediate context are the best predictors of word usages, hinting at the importance of word meaning and its grammatical function for these demographics, while for gender, topics obtained from longer contexts are better predictors for word usage. | Reflection of Demographic Background on Word Usage under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license |
d2412277 | Recently, neural network approaches for parsing have largely automated the combination of individual features, but still rely on (often a larger number of) atomic features created from human linguistic intuition, and potentially omitting important global context. To further reduce feature engineering to the bare minimum, we use bi-directional LSTM sentence representations to model a parser state with only three sentence positions, which automatically identifies important aspects of the entire sentence. This model achieves state-of-the-art results among greedy dependency parsers for English. We also introduce a novel transition system for constituency parsing which does not require binarization, and together with the above architecture, achieves state-of-the-art results among greedy parsers for both English and Chinese. | Incremental Parsing with Minimal Features Using Bi-Directional LSTM |
d16851175 | The task of Native Language Identification (NLI) is typically solved with machine learning methods, and systems make use of a wide variety of features. Some preliminary studies have been conducted to examine the effectiveness of individual features, however, no systematic study of feature interaction has been carried out. We propose a function to measure feature independence and analyze its effectiveness on a standard NLI corpus. | Measuring Feature Diversity in Native Language Identification |
d248177827 | Common studies of gender bias in NLP focus either on extrinsic bias measured by model performance on a downstream task or on intrinsic bias found in models' internal representations. However, the relationship between extrinsic and intrinsic bias is relatively unknown. In this work, we illuminate this relationship by measuring both quantities together: we debias a model during downstream fine-tuning, which reduces extrinsic bias, and measure the effect on intrinsic bias, which is operationalized as bias extractability with information-theoretic probing. Through experiments on two tasks and multiple bias metrics, we show that our intrinsic bias metric is a better indicator of debiasing than (a contextual adaptation of) the standard WEAT metric, and can also expose cases of superficial debiasing. Our framework provides a comprehensive perspective on bias in NLP models, which can be applied to deploy NLP systems in a more informed manner. | How Gender Debiasing Affects Internal Model Representations, and Why It Matters |
d256461020 | Sarcasm is prevalent in all corners of social media, posing many challenges within Natural Language Processing (NLP), particularly for sentiment analysis. Sarcasm detection remains a largely unsolved problem in many NLP tasks due to its contradictory and typically derogatory nature as a figurative language construct. With recent strides in NLP, many pre-trained language models exist that have been trained on data from specific social media platforms, i.e., Twitter. In this paper, we evaluate the efficacy of multiple sarcasm detection datasets using machine and deep learning models. We create two new datasets -a manually annotated gold standard Sarcasm Annotated Dataset (SAD) and a Silver-Standard Sarcasm-annotated Dataset (S3D). Using a combination of existing sarcasm datasets with SAD, we train a sarcasm detection model over a social-media domain pre-trained language model, BERTweet, which yields an F1-score of 78.29%. Using an Ensemble model with an underlying majority technique, we further label S3D to produce a weakly supervised dataset containing over 100, 000 tweets. We publicly release all the code, our manually annotated and weakly supervised datasets, and fine-tuned models for further research. | Utilizing Weak Supervision to Create S3D: A Sarcasm Annotated Dataset |
d248986389 | Parameter-efficient fine-tuning methods (PEFTs) offer the promise of adapting large pre-trained models while only tuning a small number of parameters. They have been shown to be competitive with full model fine-tuning for many downstream tasks. However, prior work indicates that PEFTs may not work as well for machine translation (MT), and there is no comprehensive study showing when PEFTs work for MT. We conduct a comprehensive empirical study of PEFTs for MT, considering (1) various parameter budgets, (2) a diverse set of language-pairs, and (3) different pre-trained models. We find that 'adapters', in which small feed-forward networks are added after every layer, are indeed on par with full model fine-tuning when the parameter budget corresponds to 10% of total model parameters. Nevertheless, as the number of tuned parameters decreases, the performance of PEFTs decreases. The magnitude of this decrease depends on the language pair, with PEFTs particularly struggling for distantly related language-pairs. We find that using PEFTs with a larger pre-trained model outperforms full fine-tuning with a smaller model, and for smaller training data sizes, PEFTs outperform full fine-tuning for the same pre-trained model. 1 | When does Parameter-Efficient Transfer Learning Work for Machine Translation? |
d259370786 | Jointly fine-tuning a Pre-trained Language Model (PLM) on a pre-defined set of tasks with in-context instructions has been proven to improve its generalization performance, allowing us to build a universal language model that can be deployed across task boundaries. In this work, we explore for the first time whether this attractive property of in-context instruction learning can be extended to a scenario in which tasks are fed to the target PLM in a sequential manner. The primary objective of so-called lifelong in-context instruction learning is to improve the target PLM's instance-and task-level generalization performance as it observes more tasks. DYNAINST, the proposed method to lifelong in-context instruction learning, achieves noticeable improvements in both types of generalization, nearly reaching the upper bound performance obtained through joint training. | Large-scale Lifelong Learning of In-context Instructions and How to Tackle It |
d259376480 | This study describes the model design of the NCUEE-NLP system for the SemEval-2023 NLI4CT task that focuses on multievidence natural language inference for clinical trial data. We use the LinkBERT transformer in the biomedical domain (denoted as BioLinkBERT) as our main system architecture. First, a set of sentences in clinical trial reports is extracted as evidence for premise-statement inference. This identified evidence is then used to determine the inference relation (i.e., entailment or contradiction). Finally, a soft voting ensemble mechanism is applied to enhance the system performance. For Subtask 1 on textual entailment, our best submission had an F1-score of 0.7091, ranking sixth among all 30 participating teams. For Subtask 2 on evidence retrieval, our best result obtained an F1-score of 0.7940, ranking ninth of 19 submissions. | |
d259376738 | We demonstrate a simple yet effective approach to augmenting training data for multilingual named entity recognition using machine translation. The named entity spans from the original sentences are transferred to the translations via word alignment and then filtered with the baseline recognizer to retain high quality annotations. The proposed data augmentation approach improves the baseline performance of XLM-Roberta on the multilingual dataset. | Sakura at SemEval-2023 Task 2: Data Augmentation via Translation |
d259376825 | Visual Word Sense Disambiguation (VWSD) task aims to find the most related image among 10 images to an ambiguous word in some limited textual context. In this work, we use Alt-CLIP features and a 3-layer standard transformer encoder to compare the cosine similarity between the given phrase and different images. Also, we improve our model's generalization by using a subset of LAION-5B. The best official baseline achieves 37.20% and 54.39% macro-averaged hit rate and MRR (Mean Reciprocal Rank) respectively. Our best configuration reaches 39.61% and 56.78% macro-averaged hit rate and MRR respectively. The code will be made publicly available on GitHub. | PMCoders at SemEval-2023 Task 1: RAltCLIP: Use Relative AltCLIP Features to Rank |
d259376870 | We propose a novel distantly supervised document-level biomedical relation extraction model that uses partial knowledge graphs that include the graph neighborhood of the entities appearing in each input document. Most conventional distantly supervised relation extraction methods use only the entity relations automatically annotated by using knowledge base entries. They do not fully utilize the rich information in the knowledge base, such as entities other than the target entities and the network of heterogeneous entities defined in the knowledge base. To address this issue, our model integrates the representations of the entities acquired from the neighborhood knowledge graphs with the representations of the input document. We conducted experiments on the ChemDisGene dataset using Comparative Toxicogenomics Database (CTD) for documentlevel relation extraction with respect to interactions between drugs, diseases, and genes. Experimental results confirmed the performance improvement by integrating entities and their neighborhood biochemical information from the knowledge base. 1 | Distantly Supervised Document-Level Biomedical Relation Extraction with Neighborhood Knowledge Graphs |
d259370577 | State-of-the-art techniques common to low resource Machine Translation (MT) are applied to improve MT of spoken language text to Sign Language (SL) glosses. In our experiments, we improve the performance of the transformer-based models via (1) data augmentation, (2) semi-supervised Neural Machine Translation (NMT), (3) transfer learning and (4) multilingual NMT. The proposed methods are implemented progressively on two German SL corpora containing gloss annotations. Multilingual NMT combined with data augmentation appear to be the most successful setting, yielding statistically significant improvements as measured by three automatic metrics (up to over 6 points BLEU), and confirmed via human evaluation. Our best setting outperforms all previous work that report on the same test-set and is also confirmed on a corpus of the American Sign Language (ASL). | Neural Machine Translation Methods for Translating Text to Sign Language Glosses |
d259370736 | Recently, question answering over temporal knowledge graphs (i.e., TKGQA) has been introduced and investigated, in quest of reasoning about dynamic factual knowledge. To foster research on TKGQA, a few datasets have been curated (e.g., CRONQUESTIONS and Complex-CRONQUESTIONS), and various models have been proposed based on these datasets. Nevertheless, existing efforts overlook the fact that real-life applications of TKGQA also tend to be complex in temporal granularity, i.e., the questions may concern mixed temporal granularities (e.g., both day and month). To overcome the limitation, in this paper, we motivate the notion of multi-granularity temporal question answering over knowledge graphs and present a largescale dataset for multi-granularity TKGQA, namely MULTITQ. To the best of our knowledge, MULTITQ is among the first of its kind, and compared with existing datasets on TKGQA, MULTITQ features at least two desirable aspects-ample relevant facts and multiple temporal granularities. It is expected to better reflect real-world challenges, and serve as a test bed for TKGQA models. In addition, we propose a competing baseline MultiQA over MULTITQ, which is experimentally demonstrated to be effective in dealing with TKGQA. The data and code are released at https: //github.com/czy1999/MultiTQ. | Multi-granularity Temporal Question Answering over Knowledge Graphs |
d3130692 | We describe a question answering model that applies to both images and structured knowledge bases. The model uses natural language strings to automatically assemble neural networks from a collection of composable modules. Parameters for these modules are learned jointly with network-assembly parameters via reinforcement learning, with only (world, question, answer) triples as supervision. Our approach, which we term a dynamic neural module network, achieves state-of-theart results on benchmark datasets in both visual and structured domains. | Learning to Compose Neural Networks for Question Answering |
d52156147 | Understanding and reasoning about cooking recipes is a fruitful research direction towards enabling machines to interpret procedural text. In this work, we introduce RecipeQA, a dataset for multimodal comprehension of cooking recipes. It comprises of approximately 20K instructional recipes with multiple modalities such as titles, descriptions and aligned set of images. With over 36K automatically generated question-answer pairs, we design a set of comprehension and reasoning tasks that require joint understanding of images and text, capturing the temporal flow of events and making sense of procedural knowledge. Our preliminary results indicate that RecipeQA will serve as a challenging test bed and an ideal benchmark for evaluating machine comprehension systems. The data and leaderboard are available at | RecipeQA: A Challenge Dataset for Multimodal Comprehension of Cooking Recipes |
d13692090 | Semantic hashing has become a powerful paradigm for fast similarity search in many information retrieval systems. While fairly successful, previous techniques generally require two-stage training, and the binary constraints are handled ad-hoc. In this paper, we present an end-to-end Neural Architecture for Semantic Hashing (NASH), where the binary hashing codes are treated as Bernoulli latent variables. A neural variational inference framework is proposed for training, where gradients are directly backpropagated through the discrete latent variable to optimize the hash function. We also draw connections between proposed method and rate-distortion theory, which provides a theoretical foundation for the effectiveness of the proposed framework. Experimental results on three public datasets demonstrate that our method significantly outperforms several state-of-the-art models on both unsupervised and supervised scenarios. | NASH: Toward End-to-End Neural Architecture for Generative Semantic Hashing |
d253510703 | Automated completion of open knowledge bases (Open KBs), which are constructed from triples of the form (subject phrase, relation phrase, object phrase), obtained via open information extraction (Open IE) system, are useful for discovering novel facts that may not be directly present in the text. However, research in Open KB completion (Open KBC) has so far been limited to resource-rich languages like English. Using the latest advances in multilingual Open IE, we construct the first multilingual Open KBC dataset, called mOKB6, containing facts from Wikipedia in six languages (including English). Improving the previous Open KB construction pipeline by doing multilingual coreference resolution and keeping only entitylinked triples, we create a dense Open KB. We experiment with several models for the task and observe a consistent benefit of combining languages with the help of shared embedding space as well as translations of facts. We also observe that current multilingual models struggle to remember facts seen in languages of different scripts. 1 | mOKB6: A Multilingual Open Knowledge Base Completion Benchmark |
d250211084 | In recent years, voice-controlled personal assistants have revolutionized the interaction with smart devices and mobile applications. The collected data are then used by system providers to train language models (LMs). Each spoken message reveals personal information, hence removing private information from the input sentences is necessary. Our data sanitization process relies on recognizing and replacing named entities by other words from the same class. However, this may harm LM training because privacy-transformed data is unlikely to match the test distribution. This paper aims to fill the gap by focusing on the adaptation of LMs initially trained on privacy-transformed sentences using a small amount of original untransformed data. To do so, we combine class-based LMs, which provide an effective approach to overcome data sparsity in the context of n-gram LMs, and neural LMs, which handle longer contexts and can yield better predictions. Our experiments show that training an LM on privacy-transformed data result in a relative 11% word error rate (WER) increase compared to training on the original untransformed data, and adapting that model on a limited amount of original untransformed data leads to a relative 8% WER improvement over the model trained solely on privacy-transformed data. | Adapting Language Models When Training on Privacy-Transformed Data |
d21731773 | In this paper, we introduce MADARi, a joint morphological annotation and spelling correction system for texts in Standard and Dialectal Arabic. The MADARi framework provides intuitive interfaces for annotating text and managing the annotation process of a large number of sizable documents. Morphological annotation includes indicating, for a word, in context, its baseword, clitics, part-of-speech, lemma, gloss, and dialect identification. MADARi has a suite of utilities to help with annotator productivity. For example, annotators are provided with pre-computed analyses to assist them in their task and reduce the amount of work needed to complete it. MADARi also allows annotators to query a morphological analyzer for a list of possible analyses in multiple dialects or look up previously submitted analyses. The MADARi management interface enables a lead annotator to easily manage and organize the whole annotation process remotely and concurrently. We describe the motivation, design and implementation of this interface; and we present details from a user study working with this system. | MADARi: A Web Interface for Joint Arabic Morphological Annotation and Spelling Correction |
d216641978 | The emergence of a variety of graph-based meaning representations (MRs) has sparked an important conversation about how to adequately represent semantic structure. MRs exhibit structural differences that reflect different theoretical and design considerations, presenting challenges to uniform linguistic analysis and cross-framework semantic parsing. Here, we ask the question of which design differences between MRs are meaningful and semantically-rooted, and which are superficial. We present a methodology for normalizing discrepancies between MRs at the compositional level , finding that we can normalize the majority of divergent phenomena using linguistically-grounded rules. Our work significantly increases the match in compositional structure between MRs and improves multi-task learning (MTL) in a low-resource setting, serving as a proof of concept for future broad-scale cross-MR normalization. | Normalizing Compositional Structures Across Graphbanks |
d1034121 | In an effort to develop measures of discourse level management strategies, this study examines a measure of the degree to which decisionmaking interactions consist of sequences of utterance functions that are linked in a decisionmaking routine. The measure is applied to 100 dyadic interactions elicited in both face-to-face and computer-mediated environments with systematic variation of task complexity and message-window size. Every utterance in the interactions is coded according to a system that identifies decision-makmg functions and other routine functions of utterances.Markov analyses of the coded utterances make it possible to measure the relative fi'equencies with which sequences of 2 and 3 utterances trace a path in a Markov model of the decision routine. These proportions suggest that interactions in all conditions adhere to the model, although we find greater conformity in the computer-mediated environments, which is probably due to increased processing and attmfional demands for greater efficiency, The results suggest that measures based on Markov analyses of coded interactions can provide useful measures for comparing discourse level properties, for correlating discourse features with other textual features, and for analyses of discourse management strategies. | Measuring Conformity to Discourse Routines in Decision-Making Interactions |
d201740110 | In this paper, we describe the IIT Patna's submission to WMT 2019 shared task on parallel corpus filtering. This shared task asks the participants to develop methods for scoring each parallel sentence from a given noisy parallel corpus. Quality of the scoring method is judged based on the quality of SMT and NMT systems trained on smaller set of high-quality parallel sentences sub-sampled from the original noisy corpus. This task has two language pairs. We submit for both the Nepali-English and Sinhala-English language pairs. We define fuzzy string matching score between English and the translated (into English) source based on Levenshtein distance. Based on the scores, we sub-sample two sets (having 1 million and 5 millions English tokens) of parallel sentences from each parallel corpus, and train SMT systems for development purpose only. The organizers publish the official evaluation using both SMT and NMT on the final official test set. Total 10 teams participated in the shared task and according the official evaluation, our scoring method obtains 2nd position in the team ranking for 1-million Nepali-English NMT and 5-million Sinhala-English NMT categories. | Parallel Corpus Filtering based on Fuzzy String Matching |
d258865260 | Recent works in Event Argument Extraction (EAE) have focused on improving model generalizability to cater to new events and domains. However, standard benchmarking datasets like ACE and ERE cover less than 40 event types and 25 entity-centric argument roles. Limited diversity and coverage hinder these datasets from adequately evaluating the generalizability of EAE models. In this paper, we first contribute by creating a large and diverse EAE ontology. This ontology is created by transforming FrameNet, a comprehensive semantic role labeling (SRL) dataset for EAE, by exploiting the similarity between these two tasks. Then, exhaustive human expert annotations are collected to build the ontology, concluding with 115 events and 220 argument roles, with a significant portion of roles not being entities. We utilize this ontology to further introduce GENEVA, a diverse generalizability benchmarking dataset comprising four test suites, aimed at evaluating models' ability to handle limited data and unseen event type generalization. We benchmark six EAE models from various families. The results show that owing to non-entity argument roles, even the best-performing model can only achieve 39% F1 score, indicating how GENEVA provides new challenges for generalization in EAE. Overall, our large and diverse EAE ontology can aid in creating more comprehensive future resources, while GENEVA is a challenging benchmarking dataset encouraging further research for improving generalizability in EAE. | GENEVA: Benchmarking Generalizability for Event Argument Extraction with Hundreds of Event Types and Argument Roles |
d246634531 | There have been many successful applications of sentence embedding methods. However, it has not been well understood what properties are captured in the resulting sentence embeddings depending on the supervision signals. In this paper, we focus on two types of sentence embedding methods with similar architectures and tasks: one fine-tunes pre-trained language models on the natural language inference task, and the other fine-tunes pre-trained language models on word prediction task from its definition sentence, and investigate their properties. Specifically, we compare their performances on semantic textual similarity (STS) tasks using STS datasets partitioned from two perspectives: 1) sentence source and 2) superficial similarity of the sentence pairs, and compare their performances on the downstream and probing tasks. Furthermore, we attempt to combine the two methods and demonstrate that combining the two methods yields substantially better performance than the respective methods on unsupervised STS tasks and downstream tasks. | Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals |
d218517099 | Large transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates. While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference. In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. Each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time. Partial encodings from the transformer model are shared among rerankers, providing further speed-up. When compared to a state-of-the-art transformer model, our approach reduces computation by 37% with almost no impact on accuracy, as measured on two English Question Answering datasets. | The Cascade Transformer: an Application for Efficient Answer Sentence Selection |
d252734720 | A critical component of competence in language is being able to identify relevant components of an utterance and reply appropriately. In this paper we examine the extent of such dialogue response sensitivity in pre-trained language models, conducting a series of experiments with a particular focus on sensitivity to dynamics involving phenomena of at-issueness and ellipsis. We find that models show clear sensitivity to a distinctive role of embedded clauses, and a general preference for responses that target main clause content of prior utterances. However, the results indicate mixed and generally weak trends with respect to capturing the full range of dynamics involved in targeting at-issue versus not-at-issue content. Additionally, models show fundamental limitations in grasp of the dynamics governing ellipsis, and response selections show clear interference from superficial factors that outweigh the influence of principled discourse constraints. | "No, they did not": Dialogue response dynamics in pre-trained language models |
d237485215 | Document-level entity-based extraction (EE), aiming at extracting entity-centric information such as entity roles and entity relations, is key to automatic knowledge acquisition from text corpora for various domains. Most document-level EE systems build extractive models, which struggle to model long-term dependencies among entities at the document level. To address this issue, we propose a generative framework for two document-level EE tasks: role-filler entity extraction (REE) and relation extraction (RE). We first formulate them as a template generation problem, allowing models to efficiently capture crossentity dependencies, exploit label semantics, and avoid the exponential computation complexity of identifying N-ary relations. A novel cross-attention guided copy mechanism, TOPK COPY, is incorporated into a pre-trained sequence-to-sequence model to enhance the capabilities of identifying key information in the input document. Experiments done on the MUC-4 and SCIREX dataset show new stateof-the-art results on REE (+3.26%), binary RE (+4.8%), and 4-ary RE (+2.7%) in F1 score 1 . | Document-level Entity-based Extraction as Template Generation |
d259376500 | Court Judgement Prediction with Explanation (CJPE) is a task in the field of legal analysis and evaluation, which involves predicting the outcome of a court case based on the available legal text and providing a detailed explanation of the prediction. This is an important task in the legal system as it can aid in decision-making and improve the efficiency of the court process. In this paper, we present a new approach to understanding legal texts, which are normally long documents, based on data-oriented methods. Specifically, we first try to exploit the characteristic of data to understand the legal texts. The output is then used to train the model using the Longformer architecture. Regarding the experiment, the proposed method is evaluated on the sub-task CJPE of the SemEval-2023 Task 6. Accordingly, our method achieves top 1 and top 2 on the classification task and explanation task, respectively. Furthermore, we present several open research issues for further investigations in order to improve the performance in this research field. . 2021a. Semantic segmentation of legal documents via rhetorical roles. CoRR, abs/2112.01836. | Viettel-AI at SemEval-2023 Task 6: Legal Document Understanding with Longformer for Court Judgment Prediction with Explanation |
d6472624 | We describe the work carried out by DCU on the Aspect Based Sentiment Analysis task at SemEval 2014. Our team submitted one constrained run for the restaurant domain and one for the laptop domain for sub-task B (aspect term polarity prediction), ranking highest out of 36 systems on the restaurant test set and joint highest out of 32 systems on the laptop test set. | DCU: Aspect-based Polarity Classification for SemEval Task 4 |
d243865638 | Basic-level categories (BLC) are an important psycholinguistic concept introduced by Rosch et al. (1976); they are defined as the most inclusive categories for which a concrete mental image of the category as a whole can be formed, and also as those categories which are acquired early in life. Rosch's original algorithm for detecting BLC (called cue-validity) is based on the availability of semantic features such as 'has tail' for 'cat', and has remained untested at large. An at-scale algorithm for the automatic determination of BLC exists, but it operates without Rosch-style semantic features, and is thus unable to verify Rosch's hypothesis. We present the first method for the detection of BLC at scale that makes use of Rosch-style semantic features. For both English and Mandarin, we test three methods of generating such features for any synset within Wordnet (WN): extraction of textual features from Wikipedia pages, Distributional Memory (DM) and BART. The best of our methods outperforms the current SoA in BLC detection, with an accuracy of English BLC detection of 75.0%, and of Mandarin BLC detection 80.7% on a test set. When applied to all of WordNet, our model predicts that 1,118 synsets in English Wordnet (1.4%) are BLC, far fewer than existing methods, and with a precision improvement of over 200% over these. As well as confirming the usefulness of Rosch's cue validity algorithm, we also developed and evaluated our own new indicator for BLC, which models the fact that BLC features tend to be BLC themselves. | Synthetic Textual Features for the Large-Scale Detection of Basic-level Categories in English and Mandarin |
d243831446 | The deception in the text can be of different forms in different domains, including fake news, rumor tweets, and spam emails. Irrespective of the domain, the main intent of the deceptive text is to deceit the reader. Although domain-specific deception detection exists, domain-independent deception detection can provide a holistic picture, which can be crucial to understand how deception occurs in the text. In this paper, we detect deception in a domain-independent setting using deep learning architectures. Our method outperforms the State-of-the-Art (SOTA) performance of most benchmark datasets with an overall accuracy of 93.42% and F1-Score of 93.22%. The domain-independent training allows us to capture subtler nuances of deceptive writing style. Furthermore, we analyze how much in-domain data may be helpful to accurately detect deception, especially for the cases where data may not be readily available to train. Our results and analysis indicate that there may be a universal pattern of deception lying in-between the text independent of the domain, which can create a novel area of research and open up new avenues in the field of deception detection. | A Domain-Independent Holistic Approach to Deception Detection |
d260063105 | Paraphrase detection is useful in many natural language understanding applications. Current works typically formulate this problem as a sentence pair binary classification task. However, this setup is not a good fit for many of the intended applications of paraphrase models. In particular, such applications often involve finding the closest paraphrases of the target sentence from a group of candidate sentences where they exhibit different degrees of semantic overlap with the target sentence. To apply models to this paraphrase retrieval scenario, the model must be sensitive to the degree to which two sentences are paraphrases of one another. However, many existing datasets ignore and fail to test models in this setup. In response, we propose adversarial paradigms to create evaluation datasets, which could examine the sensitivity to different degrees of semantic overlap. Empirical results show that, while paraphrase models and different sentence encoders appear successful on standard evaluations, measuring the degree of semantic overlap still remains a big challenge for them. | Testing Paraphrase Models on Recognising Sentence Pairs at Different Degrees of Semantic Overlap |
d6105163 | The PECO framework is a knowledge representation for formulating clinical questions. Queries are decomposed into four aspects, which are Patient-Problem (P), Exposure (E), Comparison (C) and Outcome (O). However, no test collection is available to evaluate such framework in information retrieval. In this work, we first present the construction of a large test collection extracted from systematic literature reviews. We then describe an analysis of the distribution of PECO elements throughout the relevant documents and propose a language modeling approach that uses these distributions as a weighting strategy. In our experiments carried out on a collection of 1.5 million documents and 423 queries, our method was found to lead to an improvement of 28% in MAP and 50% in P@5, as compared to the state-of-the-art method. | Positional Language Models for Clinical Information Retrieval |
d221996144 | Document-level relation extraction aims to extract relations among entities within a document. Different from sentence-level relation extraction, it requires reasoning over multiple sentences across a document. In this paper, we propose Graph Aggregation-and-Inference Network (GAIN) featuring double graphs. GAIN first constructs a heterogeneous mention-level graph (hMG) to model complex interaction among different mentions across the document. It also constructs an entitylevel graph (EG), based on which we propose a novel path reasoning mechanism to infer relations between entities. Experiments on the public dataset, DocRED, show GAIN achieves a significant performance improvement (2.85 on F1) over the previous state-of-the-art. Our code is available at https://github.com/ DreamInvoker/GAIN. * Equal contribution. † Corresponding author. Elias Brown [1] Elias Brown (May 9, 1793-July 7, 1857) was a U.S. Representative from Maryland. [2] Born near Baltimore, Maryland, Brown attended the common schools. … [7] He died near Baltimore, Maryland, and is interred in a private cemetery near Eldersburg, Maryland. | Double Graph Based Reasoning for Document-level Relation Extraction |
d22236307 | This opinion paper proposes the use of parallel treebank as learner corpus. We show how an L1-L2 parallel treebanki.e., parse trees of non-native sentences, aligned to the parse trees of their target hypotheses -can facilitate retrieval of sentences with specific learner errors. We argue for its benefits, in terms of corpus reuse and interoperability, over a conventional learner corpus annotated with error tags. As a proof of concept, we conduct a case study on word-order errors made by learners of Chinese as a foreign language. We report precision and recall in retrieving a range of word-order error categories from L1-L2 tree pairs annotated in the Universal Dependency framework. | L1-L2 Parallel Dependency Treebank as Learner Corpus |
d53234585 | Neural approaches to data-to-text generation generally handle rare input items using either delexicalisation or a copy mechanism. We investigate the relative impact of these two methods on two datasets (E2E and WebNLG) and using two evaluation settings. We show (i) that rare items strongly impact performance; (ii) that combining delexicalisation and copying yields the strongest improvement; (iii) that copying underperforms for rare and unseen items and (iv) that the impact of these two mechanisms greatly varies depending on how the dataset is constructed and on how it is split into train, dev and test 1 . | Handling Rare Items in Data-to-Text Generation |
d10550446 | In this shared task paper for SemEval-2014 Task 8, we show that most semantic structures can be approximated by trees through a series of almost bijective graph transformations. We transform input graphs, apply off-the-shelf methods from syntactic parsing on the resulting trees, and retrieve output graphs. Using tree approximations, we obtain good results across three semantic formalisms, with a 15.9% error reduction over a stateof-the-art semantic role labeling system on development data. Our system came in 3/6 in the shared task closed track. | Copenhagen-Malmö: Tree Approximations of Semantic Parsing Problems |
d256461346 | While automatically computing numerical scores remains the dominant paradigm in NLP system evaluation, error annotation and analysis is receiving increasing attention, with several error annotation schemes recently proposed for automatically generated text. However, there is little agreement about what error annotation schemes should look like, how many different types of errors should be distinguished and at what level of granularity. In this paper, our aim is to map out work on annotating errors in human and machine generated text, with a particular focus on error taxonomies. We describe our paper selection process, and survey the error annotation schemes reported in the papers, drawing out similarities and differences between them. Finally, we characterise the issues that would make it difficult to move from the current situation to a standardised error taxonomy for annotating errors in automatically generated text. | A Survey of Error Annotation Schemes for Human and Machine Generated Text |
d16080480 | A key challenge of designing coherent semantic ontology for spoken language understanding is to consider inter-slot relations. In practice, however, it is difficult for domain experts and professional annotators to define a coherent slot set, while considering various lexical, syntactic, and semantic dependencies. In this paper, we exploit the typed syntactic dependency theory for unsupervised induction and filling of semantics slots in spoken dialogue systems. More specifically, we build two knowledge graphs: a slot-based semantic graph, and a word-based lexical graph. To jointly consider word-to-word, word-toslot, and slot-to-slot relations, we use a random walk inference algorithm to combine the two knowledge graphs, guided by dependency grammars. The experiments show that considering inter-slot relations is crucial for generating a more coherent and compete slot set, resulting in a better spoken language understanding model, while enhancing the interpretability of semantic slots. | Jointly Modeling Inter-Slot Relations by Random Walk on Knowledge Graphs for Unsupervised Spoken Language Understanding |
d19814651 | We explore various supervised learning strategies for automated scoring of content knowledge for a large corpus of 130 different content-based questions spanning four subject areas (Science, Math, English Language Arts, and Social Studies) and containing over 230,000 responses scored by human raters. Based on our analyses, we provide specific recommendations for content scoring. These are based on patterns observed across multiple questions and assessments and are, therefore, likely to generalize to other scenarios and prove useful to the community as automated content scoring becomes more popular in schools and classrooms. | A Large Scale Quantitative Exploration of Modeling Strategies for Content Scoring |
d4892899 | Morphological segmentation for polysynthetic languages is challenging, because a word may consist of many individual morphemes and training data can be extremely scarce. Since neural sequence-to-sequence (seq2seq) models define the state of the art for morphological segmentation in high-resource settings and for (mostly) European languages, we first show that they also obtain competitive performance for Mexican polysynthetic languages in minimal-resource settings. We then propose two novel multi-task training approachesone with, one without need for external unlabeled resources-, and two corresponding data augmentation methods, improving over the neural baseline for all languages. Finally, we explore cross-lingual transfer as a third way to fortify our neural model and show that we can train one single multi-lingual model for related languages while maintaining comparable or even improved performance, thus reducing the amount of parameters by close to 75%. We provide our morphological segmentation datasets for Mexicanero, Nahuatl, Wixarika and Yorem Nokki for future research. | Fortification of Neural Morphological Segmentation Models for Polysynthetic Minimal-Resource Languages |
d462954 | The design and implementation of a paraphrase component for a natural language questlon-answer system (CO-OP) is presented. A major point made is the role of given and new information in formulating a paraphrase that differs in a meaningful way from the user's question.A description is also given of the transformational grammar used by the paraphraser to generate questions. | Paraphrasing Using Given and New Information in a Question-Answer System |
d9317139 | This paper shows how to construct semantic representations from the derivations produced by a wide-coverage CCG parser. Unlike the dependency structures returned by the parser itself, these can be used directly for semantic interpretation. We demonstrate that well-formed semantic representations can be produced for over 97% of the sentences in unseen WSJ text. We believe this is a major step towards widecoverage semantic interpretation, one of the key objectives of the field of NLP. | Wide-Coverage Semantic Representations from a CCG Parser |
d196198815 | Clinical letters are infamously impenetrable for the lay patient. This work uses neural text simplification methods to automatically improve the understandability of clinical letters for patients. We take existing neural text simplification software and augment it with a new phrase table that links complex medical terminology to simpler vocabulary by mining SNOMED-CT. In an evaluation task using crowdsourcing, we show that the results of our new system are ranked easier to understand (average rank 1.93) than using the original system (2.34) without our phrase table. We also show improvement against baselines including the original text (2.79) and using the phrase table without the neural text simplification software (2.94). Our methods can easily be transferred outside of the clinical domain by using domain-appropriate resources to provide effective neural text simplification for any domain without the need for costly annotation. | Neural Text Simplification of Clinical Letters with a Domain Specific Phrase Table |
d259833823 | Grapheme-to-phoneme conversion is an important component in many speech technologies, but until recently there were no multilingual benchmarks for this task. The third iteration of the SIGMORPHON shared task on multilingual grapheme-to-phoneme conversion features many improvements from the previous year's task (Ashby et al., 2021), including additional languages, three subtasks varying the amount of available resources, extensive quality assurance procedures, and automated error analyses. Three teams submitted a total of fifteen systems, at best achieving relative reductions of word error rate of 14% in the crosslingual subtask and 14% in the very-low resource subtask. The generally consistent result is that cross-lingual transfer substantially helps grapheme-to-phoneme modeling, but not to the same degree as in-language examples. | The SIGMORPHON 2022 Shared Task on Cross-lingual and Low-Resource Grapheme-to-Phoneme Conversion |
d218487046 | Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer useful indicators of translation errors in the generated output. While prior studies have shown that NMT models trained with label smoothing are well-calibrated on the groundtruth training data, we find that miscalibration still remains a severe challenge for NMT during inference due to the discrepancy between training and inference. By carefully designing experiments on three language pairs, our work provides in-depth analyses of the correlation between calibration and translation performance as well as linguistic properties of miscalibration and reports a number of interesting findings that might help humans better analyze, understand and improve NMT models. Based on these observations, we further propose a new graduated label smoothing method that can improve both inference calibration and translation performance. 1 | On the Inference Calibration of Neural Machine Translation |
d237485289 | Meta-learning has achieved great success in leveraging the historical learned knowledge to facilitate the learning process of the new task. However, merely learning the knowledge from the historical tasks, adopted by current meta-learning algorithms, may not generalize well to testing tasks when they are not well-supported by training tasks. This paper studies a low-resource text classification problem and bridges the gap between meta-training and meta-testing tasks by leveraging the external knowledge bases. Specifically, we propose KGML to introduce additional representation for each sentence learned from the extracted sentence-specific knowledge graph. The extensive experiments on three datasets demonstrate the effectiveness of KGML under both supervised adaptation and unsupervised adaptation settings. | Knowledge-Aware Meta-learning for Low-Resource Text Classification |
d235794968 | We present a direct speech-to-speech translation (S2ST) model that translates speech from one language to speech in another language without relying on intermediate text generation. We tackle the problem by first applying a self-supervised discrete speech encoder on the target speech and then training a sequenceto-sequence speech-to-unit translation (S2UT) model to predict the discrete representations of the target speech. When target text transcripts are available, we design a joint speech and text training framework that enables the model to generate dual modality output (speech and text) simultaneously in the same inference pass. Experiments on the Fisher Spanish-English dataset show that the proposed framework yields improvement of 6.7 BLEU compared with a baseline direct S2ST model that predicts spectrogram features. When trained without any text transcripts, our model performance is comparable to models that predict spectrograms and are trained with text supervision, showing the potential of our system for translation between unwritten languages 1 . | Direct Speech-to-Speech Translation With Discrete Units |
d36171289 | Book Reviews Unification Grammars | |
d14038457 | The SignSpeak project is the first step in taking sign language recognition and translation to levels already obtained in automatic speech recognition or statistical machine translation of spoken languages. Deaf communities revolve around sign languages as their natural means of communication. Although signers can communicate without problems amongst themselves, there is a serious challenge for the deaf community in trying to integrate into educational, social and work environments. The overall goal of SignSpeak is to develop a new vision-based technology for recognizing and translating continuous sign language to text (i.e. provide Video-to-Text technologies), in order to provide new e-Services to the deaf community and improve their communication with hearing people. New knowledge about the nature of sign language structure from the perspective of machine recognition of continuous sign language will lead to a breakthrough in the development of a new vision-based technology for continuous sign language recognition and translation. Existing and new publicly available corpora will be used to evaluate the research progress throughout the whole project. | Scientific understanding and vision-based technological development for continuous sign language recognition and translation -www.signspeak.eu |
d202677244 | In this work we describe the system from Natural Language Processing group at Arizona State University for the TextGraphs 2019 Shared Task. The task focuses on Explanation Regeneration, an intermediate step towards general multi-hop inference on large graphs. Our approach consists of modeling the explanation regeneration task as a learning to rank problem, for which we use state-of-theart language models and explore dataset preparation techniques. We utilize an iterative reranking based approach to further improve the rankings. Our system secured 2nd rank in the task with a mean average precision (MAP) of 41.3% on the test set. | ASU at TextGraphs 2019 Shared Task: Explanation ReGeneration using Language Models and Iterative Re-Ranking |
d202763602 | In this paper, we focus on natural language video localization: localizing (i.e., grounding) a natural language description in a long and untrimmed video sequence. All currently published models for addressing this problem can be categorized into two types: (i) top-down approach: it does classification and regression for a set of pre-cut video segment candidates; (ii) bottom-up approach: it directly predicts probabilities for each video frame as the temporal boundaries (i.e., start and end time point). However, both two approaches suffer several limitations: the former is computationintensive for densely placed candidates, while the latter has trailed the performance of the top-down counterpart thus far. To this end, we propose a novel dense bottom-up framework: DEnse Bottom-Up Grounding (DEBUG). DE-BUG regards all frames falling in the ground truth segment as foreground, and each foreground frame regresses the unique distances from its location to bi-directional ground truth boundaries. Extensive experiments on three challenging benchmarks (TACoS, Charades-STA, and ActivityNet Captions) show that DE-BUG is able to match the speed of bottom-up models while surpassing the performance of the state-of-the-art top-down models. | DEBUG: A Dense Bottom-Up Grounding Approach for Natural Language Video Localization |
d16079627 | In this paper, we apply the concept of pretraining to hidden-unit conditional random fields (HUCRFs) to enable learning on unlabeled data. We present a simple yet effective pre-training technique that learns to associate words with their clusters, which are obtained in an unsupervised manner. The learned parameters are then used to initialize the supervised learning process. We also propose a word clustering technique based on canonical correlation analysis (CCA) that is sensitive to multiple word senses, to further improve the accuracy within the proposed framework. We report consistent gains over standard conditional random fields (CRFs) and HUCRFs without pre-training in semantic tagging, named entity recognition (NER), and part-of-speech (POS) tagging tasks, which could indicate the task independent nature of the proposed technique. | Pre-training of Hidden-Unit CRFs |
d253080367 | Controllable text simplification is a crucial assistive technique for language learning and teaching. One of the primary factors hindering its advancement is the lack of a corpus annotated with sentence difficulty levels based on language ability descriptions. To address this problem, we created the CEFR-based Sentence Profile (CEFR-SP) corpus, containing 17k English sentences annotated with the levels based on the Common European Framework of Reference for Languages assigned by English-education professionals. In addition, we propose a sentence-level assessment model to handle unbalanced level distribution because the most basic and highly proficient sentences are naturally scarce. In the experiments in this study, our method achieved a macro-F1 score of 84.5% in the level assessment, thus outperforming strong baselines employed in readability assessment. | CEFR-Based Sentence Difficulty Annotation and Assessment |
d222177485 | We investigate the following question for machine translation (MT): can we develop a single universal MT model to serve as the common seed and obtain derivative and improved models on arbitrary language pairs? We propose mRASP, an approach to pre-train a universal multilingual neural machine translation model. Our key idea in mRASP is its novel technique of random aligned substitution, which brings words and phrases with similar meanings across multiple languages closer in the representation space. We pre-train a mRASP model on 32 language pairs jointly with only public datasets. The model is then fine-tuned on downstream language pairs to obtain specialized MT models. We carry out extensive experiments on 42 translation directions across a diverse settings, including low, medium, rich resource, and as well as transferring to exotic language pairs. Experimental results demonstrate that mRASP achieves significant performance improvement compared to directly training on those target pairs. It is the first time to verify that multiple lowresource language pairs can be utilized to improve rich resource MT. Surprisingly, mRASP is even able to improve the translation quality on exotic languages that never occur in the pretraining corpus. Code, data, and pre-trained models are available at https://github. com/linzehui/mRASP. | Pre-training Multilingual Neural Machine Translation by Leveraging Alignment Information |
d236459945 | Text style transfer aims to alter the style (e.g., sentiment) of a sentence while preserving its content. A common approach is to map a given sentence to content representation that is free of style, and the content representation is fed to a decoder with a target style. Previous methods in filtering style completely remove tokens with style at the token level, which incurs the loss of content information. In this paper, we propose to enhance content preservation by implicitly removing the style information of each token with reverse attention, and thereby retain the content. Furthermore, we fuse content information when building the target style representation, making it dynamic with respect to the content. Our method creates not only styleindependent content representation, but also content-dependent style representation in transferring style. Empirical results show that our method outperforms the state-of-the-art baselines by a large margin in terms of content preservation. In addition, it is also competitive in terms of style transfer accuracy and fluency. | Enhancing Content Preservation in Text Style Transfer Using Reverse Attention and Conditional Layer Normalization |
d12964363 | We propose a model for Chinese poem generation based on recurrent neural networks which we argue is ideally suited to capturing poetic content and form. Our generator jointly performs content selection ("what to say") and surface realization ("how to say") by learning representations of individual characters, and their combinations into one or more lines as well as how these mutually reinforce and constrain each other. Poem lines are generated incrementally by taking into account the entire history of what has been generated so far rather than the limited horizon imposed by the previous line or lexical n-grams. Experimental results show that our model outperforms competitive Chinese poetry generation systems using both automatic and manual evaluation methods. | Chinese Poetry Generation with Recurrent Neural Networks |
d12973103 | Target task matched parallel corpora are required for statistical translation model training. However, training corpora sometimes include both target task matched and unmatched sentences. In such a case, training set selection can reduce the size of the translation model. In this paper, we propose a training set selection method for translation model training using linear translation model interpolation and a language model technique. According to the experimental results, the proposed method reduces the translation model size by 50% and improves BLEU score by 1.76% in comparison with a baseline training corpus usage. | Method of Selecting Training Data to Build a Compact and Efficient Translation Model |
d259203682 | Most existing text generation models follow the sequence-to-sequence paradigm. Generative Grammar suggests that humans generate natural language texts by learning language grammar. We propose a syntax-guided generation schema, which generates the sequence guided by a constituency parse tree in a topdown direction. The decoding process can be decomposed into two parts: (1) predicting the infilling texts for each constituent in the lexicalized syntax context given the source sentence;(2) mapping and expanding each constituent to construct the next-level syntax context. Accordingly, we propose a structural beam search method to find possible syntax structures hierarchically. Experiments on paraphrase generation and machine translation show that the proposed method outperforms autoregressive baselines, while also demonstrating effectiveness in terms of interpretability, controllability, and diversity. | Explicit Syntactic Guidance for Neural Text Generation |
d7380788 | We present an extensive study on the problem of detecting polarity of words. We consider the polarity of a word to be either positive or negative. For example, words such as good, beautiful, and wonderful are considered as positive words; whereas words such as bad, ugly, and sad are considered negative words. We treat polarity detection as a semi-supervised label propagation problem in a graph. In the graph, each node represents a word whose polarity is to be determined. Each weighted edge encodes a relation that exists between two words. Each node (word) can have two labels: positive or negative. We study this framework in two different resource availability scenarios using WordNet and OpenOffice thesaurus when WordNet is not available. We report our results on three different languages: English, French, and Hindi. Our results indicate that label propagation improves significantly over the baseline and other semisupervised learning methods like Mincuts and Randomized Mincuts for this task. | Semi-Supervised Polarity Lexicon Induction |
d11657346 | We present AWATIF, a multi-genre corpus of Modern Standard Arabic (MSA) labeled for subjectivity and sentiment analysis (SSA) at the sentence level. The corpus is labeled using both regular as well as crowd sourcing methods under three different conditions with two types of annotation guidelines. We describe the sub-corpora constituting the corpus and provide examples from the various SSA categories. In the process, we present our linguistically-motivated and genre-nuanced annotation guidelines and provide evidence showing their impact on the labeling task. | AWATIF: A Multi-Genre Corpus for Modern Standard Arabic Subjectivity and Sentiment Analysis |
d11696905 | Dialectal Arabic (DA) refers to the day-to-day vernaculars spoken in the Arab world. DA lives side-by-side with the official language, Modern Standard Arabic (MSA). DA differs from MSA on all levels of linguistic representation, from phonology and morphology to lexicon and syntax. Unlike MSA, DA has no standard orthography since there are no Arabic dialect academies, nor is there a large edited body of dialectal literature that follows the same spelling standard. In this paper, we present CODA, a conventional orthography for dialectal Arabic; it is designed primarily for the purpose of developing computational models of Arabic dialects. We explain the design principles of CODA and provide a detailed description of its guidelines as applied to Egyptian Arabic. | Conventional Orthography for Dialectal Arabic |
d9612196 | This demonstration presents a highperformance syntactic and semantic dependency parser. The system consists of a pipeline of modules that carry out the tokenization, lemmatization, part-of-speech tagging, dependency parsing, and semantic role labeling of a sentence. The system's two main components draw on improved versions of a state-of-the-art dependency parser (Bohnet, 2009) and semantic role labeler (Björkelund et al., 2009) developed independently by the authors.The system takes a sentence as input and produces a syntactic and semantic annotation using the CoNLL 2009 format. The processing time needed for a sentence typically ranges from 10 to 1000 milliseconds. The predicate-argument structures in the final output are visualized in the form of segments, which are more intuitive for a user. | A High-Performance Syntactic and Semantic Dependency Parser |
d219307603 | In Chinese text, discourse connectives constitute a major linguistic device available for a writer to explicitly indicate the structure of a discourse. This set of discourse connectives, consisting of a few hundred entries in modern Chinese, is relatively stable and domain independent. In a recently published paper [T'sou 1996], a computational procedure was introduced to generate the abstract of an input text using mainly the discourse connectives appearing in the text. This paper attempts to demonstrate the validity of this approach to full-text abstraction by means of an evaluation method, which compares human efforts in text abstraction with the performance of an experimental system called ACFAS. Specifically, our concern is about the relationship between the perceived importance of each individual sentence as judged by human beings and the sentences containing discourse connectives within an argumentative discourse. | Human Judgment as a Basis for Evaluation of Discourse-Connective-Based Full-Text Abstraction in Chinese |
d260063170 | While modern language models can generate a scripted scene in the format of a play, movie, or video game cutscene the quality of machine generated text remains behind that of human authors. In this work, we focus on one aspect of this quality gap; generating text in the style of an arbitrary and unseen character. We propose the Style Adaptive Semiparametric Scriptwriter (SASS) which leverages an adaptive weighted style memory to generate dialog lines in accordance with a character's speaking patterns. Using the LIGHT dataset as well as a new corpus of scripts from twenty-three AAA video games, we show that SASS not only outperforms similar models but in some cases can also be used in conjunction with them to yield further improvement. * These authors contributed equally to this work. 1 The term "AAA" refers to multi-million dollar budget productions often with hundreds of highly specialized contributors. | Generating Video Game Scripts with Style |
d227153162 | Plumitifs (dockets) were initially a tool for law clerks. | Generating Intelligible Plumitifs Descriptions: Use Case Application with Ethical Considerations |
d259370811 | Recent advances in pre-trained language models (PLMs) have facilitated the development of commonsense reasoning tasks. However, existing methods rely on multi-hop knowledge retrieval and thus suffer low accuracy due to embedded noise in the acquired knowledge. In addition, these methods often attain high computational costs and nontrivial knowledge loss because they encode the knowledge independently of the PLM, making it less relevant to the task and resulting in a poor local optimum. In this work, we propose Multi-View Knowledge Retrieval with Prompt Tuning (MVP-Tuning). Our MVP-Tuning leverages similar questionanswer pairs in training set to improve knowledge retrieval and employs a single prompttuned PLM to model knowledge and input text jointly. We conduct our experiments on five commonsense reasoning QA benchmarks to show that MVP-Tuning outperforms all other baselines in 4 out of 5 datasets with only as most 2% trainable parameters. The ensemble of our MVP-Tuning models even gets a new state-of-the-art performance on OpenBookQA and is ranked first place on the leaderboard 1 . Our code and data are available 2 . | MVP-Tuning: Multi-View Knowledge Retrieval with Prompt Tuning for Commonsense Reasoning |
d10894148 | We demonstrate how supervised discriminative machine learning techniques can be used to automate the assessment of 'English as a Second or Other Language' (ESOL) examination scripts. In particular, we use rank preference learning to explicitly model the grade relationships between scripts. A number of different features are extracted and ablation tests are used to investigate their contribution to overall performance. A comparison between regression and rank preference models further supports our method. Experimental results on the first publically available dataset show that our system can achieve levels of performance close to the upper bound for the task, as defined by the agreement between human examiners on the same corpus. Finally, using a set of 'outlier' texts, we test the validity of our model and identify cases where the model's scores diverge from that of a human examiner. | A New Dataset and Method for Automatically Grading ESOL Texts |
d32945263 | This paper develops a learnability argument for strict domination by looking at the generalization error of learners trained on OT and HG target grammars. The argument is based on both a review of error bounds in the recent statistical learning literature and simulation results on realistic phonological test cases. | Statistical learning theory and linguistic typology: a learnability perspective on OT's strict dominatioń |
d1282628 | Voice conversion is the task of transforming a source speaker's voice so that it sounds like a target speaker's voice. We present a GPUfriendly local regression model for voice conversion that is capable of converting speech in real-time and achieves state-of-the-art accuracy on this task. Our model uses a new approximation for computing local regression coefficients that is explicitly designed to preserve memory locality. As a result, our inference procedure is amenable to efficient implementation on the GPU. Our approach is more than 10X faster than a highly optimized CPUbased implementation, and is able to convert speech 2.7X faster than real-time. | GPU-Friendly Local Regression for Voice Conversion |
d53417682 | Quantifying and predicting morphological productivity is a long-standing challenge in corpus linguistics and psycholinguistics. The same challenge reappears in natural language processing in the context of handling words that were not seen in the training set (out-ofvocabulary, or OOV, words). Prior research showed that a good indicator of the productivity of a morpheme is the number of words involving it that occur exactly once (the hapax legomena). A technical connection was adduced between this result and Good-Turing smoothing, which assigns probability mass to unseen events on the basis of the simplifying assumption that word frequencies are stationary. In a large-scale study of 133 affixes in Wikipedia, we develop evidence that success in fact depends on tapping the frequency range in which the assumptions of Good-Turing are violated. | On hapax legomena and morphological productivity |
d236486106 | We describe our submission to the IWSLT 2021 shared task 1 on simultaneous text-to-text English-German translation. Our system is based on the re-translation approach where the agent re-translates the whole source prefix each time it receives a new source token. This approach has the advantage of being able to use a standard neural machine translation (NMT) inference engine with beam search, however, there is a risk that incompatibility between successive re-translations will degrade the output. To improve the quality of the translations, we experiment with various approaches: we use a fixed size wait at the beginning of the sentence, we use a language model score to detect translatable units, and we apply dynamic masking to determine when the translation is unstable. We find that a combination of dynamic masking and language model score obtains the best latency-quality trade-off. | The University of Edinburgh's Submission to the IWSLT21 Simultaneous Translation Task |
d238353985 | This paper describes our approach (ur-iw-hnt) for the Shared Task of GermEval2021 to identify toxic, engaging, and fact-claiming comments. We submitted three runs using an ensembling strategy by majority (hard) voting with multiple different BERT models of three different types: German-based, Twitter-based, and multilingual models. All ensemble models outperform single models, while BERTweet is the winner of all individual models in every subtask. Twitter-based models perform better than GermanBERT models, and multilingual models perform worse but by a small margin. | ur-iw-hnt at GermEval 2021: An Ensembling Strategy with Multiple BERT Models |
d204848204 | We present a system for Natural Language Inference which uses a dynamic semantics converter from abstract syntax trees to Coq types. It combines the fine-grainedness of a dynamic semantics system with the powerfulness of a state-of-the-art proof assistant. We evaluate the system on all sections of the FraCaS test suite, excluding section 6. This is the first system that does a complete run on the anaphora and ellipsis sections of the FraCaS. It has a better overall accuracy than any previous system. 2 The degree parameter assumption is not new in the formal semantics literature(Cresswell, 1976;Heim, 2000;Kennedy, 2007;Chatzikyriakidis and Luo, 2017) among many others. The specific details and computational imple- | A Wide-Coverage Symbolic Natural Language Inference System Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE. Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE. Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE. Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE. Submission ***. Confidential Review Copy. DO NOT DISTRIBUTE |
d2612947 | We introduce a composite deep neural network architecture for supervised and language independent context sensitive lemmatization. The proposed method considers the task as to identify the correct edit tree representing the transformation between a word-lemma pair. To find the lemma of a surface word, we exploit two successive bidirectional gated recurrent structures -the first one is used to extract the character level dependencies and the next one captures the contextual information of the given word. The key advantages of our model compared to the state-of-the-art lemmatizers such as Lemming and Morfette are -(i) it is independent of human decided features (ii) except the gold lemma, no other expensive morphological attribute is required for joint learning. We evaluate the lemmatizer on nine languages -Bengali, Catalan, Dutch, Hindi, Hungarian, Italian, Latin, Romanian and Spanish. It is found that except Bengali, the proposed method outperforms Lemming and Morfette on the other languages. To train the model on Bengali, we develop a gold lemma annotated dataset 1 (having 1, 702 sentences with a total of 20, 257 word tokens), which is an additional contribution of this work. | Context Sensitive Lemmatization Using Two Successive Bidirectional Gated Recurrent Networks |
d2665828 | We achieved a state of the art performance in statistical machine translation by using a large number of features with an online large-margin training algorithm. The millions of parameters were tuned only on a small development set consisting of less than 1K sentences. Experiments on Arabic-to-English translation indicated that a model trained with sparse binary features outperformed a conventional SMT system with a small number of features. | Online Large-Margin Training for Statistical Machine Translation |
d253761969 | In the few works that have used NLP to study literary quality, sentiment and emotion analysis have often been considered valuable sources of information. At the same time, the idea that the nature and polarity of the sentiments expressed by a novel might have something to do with its perceived quality seems limited at best. In this paper, we argue that the fractality of narratives, specifically the longterm memory of their sentiment arcs, rather than their simple shape or average valence, might play an important role in the perception of literary quality by a human audience. In particular, we argue that such measure can help distinguish Nobel-winning writers from control groups in a recent corpus of English language novels. To test this hypothesis, we present the results from two studies: (i) a probability distribution test, where we compute the probability of seeing a title from a Nobel laureate at different levels of arc fractality; (ii) a classification test, where we use several machine learning algorithms to measure the predictive power of both sentiment arcs and their fractality measure. Our findings seem to indicate that despite the competitive and complex nature of the task, the populations of Nobel and non-Nobel laureates seem to behave differently and can to some extent be told apart by a classifier. | The fractality of sentiment arcs for literary quality assessment: The case of Nobel laureates |
d258947155 | Figure 1: An overview of Finspector. Users can launch Finspector in a Python notebook (e.g., Jupyter). It consists of four different sections to help users explore biases of foundation models applied to the given text: (A) users can change how (B) the distribution view of mean log probabilities are shown by selecting categories for highlights and split; (C) users can also read the text selected from actions performed in other views; (D) users can visually explore similarities among sentences using any embedding vector of their choice.AbstractPre-trained transformer-based language models are becoming increasingly popular due to their exceptional performance on various benchmarks. However, concerns persist regarding the presence of hidden biases within these models, which can lead to discriminatory outcomes and reinforce harmful stereotypes. To address this issue, we propose Finspector, a human-centered visual inspection tool designed to detect biases in different categories through log-likelihood scores generated by language models. The goal of the tool is to enable researchers to easily identify potential biases using visual analytics, ultimately contributing to a fairer and more just deployment of these models in both academic and indus-trial settings. Finspector is available at https: //github.com/IBM/finspector. | Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models |
d7130757 | The Textual Entailment task has become influential in NLP and many researchers have become interested in applying it to other tasks. However, the two major issues emerging from this body of work are the fact that NLP applications need systems that (1) attain results which are not corpus dependent and (2) assume that the text for entailment cannot be incorrect or even contradictory. In this paper we propose a system which decomposes the text into chunks via a shallow text analysis, and determines the entailment relationship by matching the information contained in the is − a pattern. The results show that the method is able to cope with the two requirements above. | Determining is-a relationships for Textual Entailment |
d235482360 | 漢語及物化的大數據研究 A Data Scientific Study of Transitivization in Chinese 摘要 本文從資料科學的角度來考察漢語中一個新興的現象「及物化」：亦即原本以動前介詞 組引介域內論元的謂語(如「為人民服務」)轉化為直接引介動後賓語的謂語(如「服務人 民」)。我們認為這個現象其實是一種復古的趨勢，如古漢語的為動式「壯士死知己」 即「壯士為知己而死」之意，是一種隱性的輕動詞用法。值得一提的是，這種趨勢仍處 於變動之中：它可能逐步消亡，也可能引發爆炸性的發展(如同「語言癌」一般)；這便 | |
d247593812 | We introduce and study the task of clickbait spoiling: generating a short text that satisfies the curiosity induced by a clickbait post. Clickbait links to a web page and advertises its contents by arousing curiosity instead of providing an informative summary. Our contributions are approaches to classify the type of spoiler needed (i.e., a phrase or a passage), and to generate appropriate spoilers. A large-scale evaluation and error analysis on a new corpus of 5,000 manually spoiled clickbait poststhe Webis Clickbait Spoiling Corpus 2022shows that our spoiler type classifier achieves an accuracy of 80%, while the question answering model DeBERTa-large outperforms all others in generating spoilers for both types. | Clickbait Spoiling via Question Answering and Passage Retrieval |
d9911858 | Arabizi is Arabic text that is written using Latin characters. Arabizi is used to present both Modern Standard Arabic (MSA) or Arabic dialects. It is commonly used in informal settings such as social networking sites and is often with mixed with English. In this paper we address the problems of: identifying Arabizi in text and converting it to Arabic characters. We used word and sequence-level features to identify Arabizi that is mixed with English. We achieved an identification accuracy of 98.5%. As for conversion, we used transliteration mining with language modeling to generate equivalent Arabic text. We achieved 88.7% conversion accuracy, with roughly a third of errors being spelling and morphological variants of the forms in ground truth. | Arabizi Detection and Conversion to Arabic |
d44168790 | Storyline generation aims to extract events described on news articles under a certain topic and reveal how those events evolve over time. Most existing approaches first train supervised models to extract events from news articles published in different time periods and then link relevant events into coherent stories. They are domain dependent and cannot deal with unseen event types. To tackle this problem, approaches based on probabilistic graphic models jointly model the generations of events and storylines without annotated data. However, the parameter inference procedure is too complex and models often require long time to converge. In this paper, we propose a novel neural network based approach to extract structured representations and evolution patterns of storylines without using annotated data. In this model, title and main body of a news article are assumed to share the similar storyline distribution. Moreover, similar documents described in neighboring time periods are assumed to share similar storyline distributions. Based on these assumptions, structured representations and evolution patterns of storylines can be extracted. The proposed model has been evaluated on three news corpora and the experimental results show that it outperforms state-of-the-art approaches accuracy and efficiency. | Neural Storyline Extraction Model for Storyline Generation from News Articles |
d2329174 | Assigning a positive or negative score to a word out of context (i.e. a word's prior polarity) is a challenging task for sentiment analysis. In the literature, various approaches based on SentiWordNet have been proposed. In this paper, we compare the most often used techniques together with newly proposed ones and incorporate all of them in a learning framework to see whether blending them can further improve the estimation of prior polarity scores. Using two different versions of Sen-tiWordNet and testing regression and classification models across tasks and datasets, our learning approach consistently outperforms the single metrics, providing a new state-ofthe-art approach in computing words' prior polarity for sentiment analysis. We conclude our investigation showing interesting biases in calculated prior polarity scores when word Part of Speech and annotator gender are considered. | Sentiment Analysis: How to Derive Prior Polarities from SentiWordNet |
d254823129 | End-to-End speech-to-speech translation (S2ST) is generally evaluated with text-based metrics. This means that generated speech has to be automatically transcribed, making the evaluation dependent on the availability and quality of automatic speech recognition (ASR) systems. | BLASER: A Text-Free Speech-to-Speech Translation Evaluation Metric |
d253510989 | Machine translation technology has made great progress in recent years, but it cannot guarantee error-free results. Human translators perform post-editing on machine translations to correct errors in the scene of computer-aided translation. In favor of expediting the post-editing process, many works have investigated machine translation in interactive modes, in which machines can automatically refine the rest of translations constrained by human's edits. Translation Suggestion (TS), as an interactive mode to assist human translators, requires machines to generate alternatives for specific incorrect words or phrases selected by human translators. In this paper, we utilize the parameterized objective function of neural machine translation (NMT) and propose a novel constrained decoding algorithm, namely Prefix-Suffix Guided Decoding (PSGD), to deal with the TS problem without additional training. Compared to the state-of-the-art lexically constrained decoding method, PSGD improves translation quality by an average of 10.87 BLEU and 8.62 BLEU on the WeTS 1 and the WMT 2022 Translation Suggestion datasets 2 , respectively, and reduces decoding time overhead by an average of 63.4% tested on the WMT translation datasets. Furthermore, on both of the TS benchmark datasets, it is superior to other supervised learning systems trained with TS annotated data. | Easy Guided Decoding in Providing Suggestions for Interactive Machine Translation |
d7665329 | This paper introduces a new task on Multilingual and Cross-lingual Semantic Word Similarity which measures the semantic similarity of word pairs within and across five languages: English, Farsi, German, Italian and Spanish. High quality datasets were manually curated for the five languages with high inter-annotator agreements (consistently in the 0.9 ballpark). These were used for semi-automatic construction of ten cross-lingual datasets. 17 teams participated in the task, submitting 24 systems in subtask 1 and 14 systems in subtask 2. Results show that systems that combine statistical knowledge from text corpora, in the form of word embeddings, and external knowledge from lexical resources are best performers in both subtasks. More information can be found on the task website: http://alt.qcri. org/semeval2017/task2/ . | SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity |
d44080975 | In this paper, we describe Alibaba's participating system in the semEval-2018 | NAI-SEA at SemEval-2018 Task 5: An Event Search System |
d38540414 | We present a corpus of sentences from news articles that are annotated as general or specific. We employed annotators on Amazon Mechanical Turk to mark sentences from three kinds of news articles-reports on events, finance news and science journalism. We introduce the resulting corpus, with focus on annotator agreement, proportion of general/specific sentences in the articles and results for automatic classification of the two sentence types. | A Corpus of General and Specific Sentences from News |
d21724135 | Story comprehension requires a deep semantic understanding of the narrative, making it a challenging task. Inspired by previous studies on ROC Story Cloze Test, we propose a novel method, tracking various semantic aspects with external neural memory chains while encouraging each to focus on a particular semantic aspect. Evaluated on the task of story ending prediction, our model demonstrates superior performance to a collection of competitive baselines, setting a new state of the art. 1 | Narrative Modeling with Memory Chains and Semantic Supervision |
d252819130 | Taxonomy is a graph of terms organized hierarchically using is-a (hypernymy) relations. We suggest novel candidate-free task formulation for the taxonomy enrichment task. To solve the task, we leverage lexical knowledge from the pre-trained models to predict new words missing in the taxonomic resource. We propose a method that combines graph-, and text-based contextualized representations from transformer networks to predict new entries to the taxonomy. We have evaluated the method suggested for this task against text-only baselines based on BERT and fastText representations. The results demonstrate that incorporation of graph embedding is beneficial in the task of hyponym prediction using contextualized models. We hope the new challenging task will foster further research in automatic text graph construction methods. | Cross-modal Contextualized Hidden State Projection Method for Expanding of Taxonomic Graphs |
d252819295 | The present paper describes the architecture of a novel Multi-Layer Long Text Summarizer (MLLTS) system proposed for the task of creative writing summarization. Typically, such writings are very long, often spanning over 100 pages. Summarizers available online are either not equipped enough to handle long texts, or even if they are able to generate the summary, the quality is poor. The proposed MLLTS system handles the difficulty by splitting the text into several parts. Each part is then subjected to different existing summarizers. A multi-layer network is constructed by establishing linkages between the different parts. During training phases, several hyper-parameters are fine-tuned. The system achieved very good ROUGE scores on the test data supplied for the contest. | Summarization of Long Input Texts Using Multi-Layer Neural Network |
d218498322 | We investigate the use of NLP as a measure of the cognitive processes involved in storytelling, contrasting imagination and recollection of events. To facilitate this, we collect and release HIPPOCORPUS, a dataset of 7,000 stories about imagined and recalled events.We introduce a measure of narrative flow and use this to examine the narratives for imagined and recalled events. Additionally, we measure the differential recruitment of knowledge attributed to semantic memory versus episodic memory(Tulving, 1972)for imagined and recalled storytelling by comparing the frequency of descriptions of general commonsense events with more specific realis events.Our analyses show that imagined stories have a substantially more linear narrative flow, compared to recalled stories in which adjacent sentences are more disconnected. In addition, while recalled stories rely more on autobiographical events based on episodic memory, imagined stories express more commonsense knowledge based on semantic memory. Finally, our measures reveal the effect of narrativization of memories in stories (e.g., stories about frequently recalled memories flow more linearly;Bartlett, 1932). Our findings highlight the potential of using NLP tools to study the traces of human cognition in language. | Recollection versus Imagination: Exploring Human Memory and Cognition via Neural Language Models |
d258463966 | The early purpose of chatlog (conversation) disentanglement is to separate intermingled messages into detached conversations for easier information following and relevant information retrieving from simultaneous messages. Thus, the problem has been modeled as predicting whether two messages come from the samethread. While the previous study by(Jiang et al., 2018)seems to perform well on samethread prediction, we find that it is because the data are randomly split into training and test sets, resulting overlapping of topics in training and testing sets. When data is split by time order, the performance of existing models drop significantly. In this study, we consider the problem of direct reply predication task and study different message pair classification models for the task. We argue that independent message encoders could better represent messages to capture their interaction than shared message encoders especially for direct-reply prediction task. We also find that BERT model performs well with small datasets, while other models may outperform BERT with large datasets. | Chat-log Disentanglement via Same-Thread Classification and Direct-Reply Prediction |
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