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ACL
|
Program Transfer for Answering Complex Questions over Knowledge Bases
|
Program induction for answering complex questions over knowledge bases (KBs) aims to decompose a question into a multi-step program, whose execution against the KB produces the final answer. Learning to induce programs relies on a large number of parallel question-program pairs for the given KB. However, for most KBs, the gold program annotations are usually lacking, making learning difficult. In this paper, we propose the approach of program transfer, which aims to leverage the valuable program annotations on the rich-resourced KBs as external supervision signals to aid program induction for the low-resourced KBs that lack program annotations. For program transfer, we design a novel two-stage parsing framework with an efficient ontology-guided pruning strategy. First, a sketch parser translates the question into a high-level program sketch, which is the composition of functions. Second, given the question and sketch, an argument parser searches the detailed arguments from the KB for functions. During the searching, we incorporate the KB ontology to prune the search space. The experiments on ComplexWebQuestions and WebQuestionSP show that our method outperforms SOTA methods significantly, demonstrating the effectiveness of program transfer and our framework. Our codes and datasets can be obtained from https://github.com/THU-KEG/ProgramTransfer.
|
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"program induction for answering complex questions over knowledge bases ( kbs ) aims to decompose a question into a multi - step program , whose execution against the kb produces the final answer .",
"learning to induce programs relies on a large number of parallel question - program pairs for the given kb .",
"however , for most kbs , the gold program annotations are usually lacking , making learning difficult .",
"in this paper , we propose the approach of program transfer , which aims to leverage the valuable program annotations on the rich - resourced kbs as external supervision signals to aid program induction for the low - resourced kbs that lack program annotations .",
"for program transfer , we design a novel two - stage parsing framework with an efficient ontology - guided pruning strategy .",
"first , a sketch parser translates the question into a high - level program sketch , which is the composition of functions .",
"second , given the question and sketch , an argument parser searches the detailed arguments from the kb for functions .",
"during the searching , we incorporate the kb ontology to prune the search space .",
"the experiments on complexwebquestions and webquestionsp show that our method outperforms sota methods significantly , demonstrating the effectiveness of program transfer and our framework .",
"our codes and datasets can be obtained from https : / / github . com / thu - keg / programtransfer ."
] |
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ACL
|
Are Natural Language Inference Models IMPPRESsive? Learning IMPlicature and PRESupposition
|
Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer whether a sentence entails another. However, the ability of NLI models to make pragmatic inferences remains understudied. We create an IMPlicature and PRESupposition diagnostic dataset (IMPPRES), consisting of 32K semi-automatically generated sentence pairs illustrating well-studied pragmatic inference types. We use IMPPRES to evaluate whether BERT, InferSent, and BOW NLI models trained on MultiNLI (Williams et al., 2018) learn to make pragmatic inferences. Although MultiNLI appears to contain very few pairs illustrating these inference types, we find that BERT learns to draw pragmatic inferences. It reliably treats scalar implicatures triggered by “some” as entailments. For some presupposition triggers like “only”, BERT reliably recognizes the presupposition as an entailment, even when the trigger is embedded under an entailment canceling operator like negation. BOW and InferSent show weaker evidence of pragmatic reasoning. We conclude that NLI training encourages models to learn some, but not all, pragmatic inferences.
|
1a6285faf0918175c1ea9e0b7c8ea82e
| 2,020
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[
"natural language inference ( nli ) is an increasingly important task for natural language understanding , which requires one to infer whether a sentence entails another .",
"however , the ability of nli models to make pragmatic inferences remains understudied .",
"we create an implicature and presupposition diagnostic dataset ( imppres ) , consisting of 32k semi - automatically generated sentence pairs illustrating well - studied pragmatic inference types .",
"we use imppres to evaluate whether bert , infersent , and bow nli models trained on multinli ( williams et al . , 2018 ) learn to make pragmatic inferences .",
"although multinli appears to contain very few pairs illustrating these inference types , we find that bert learns to draw pragmatic inferences .",
"it reliably treats scalar implicatures triggered by “ some ” as entailments .",
"for some presupposition triggers like “ only ” , bert reliably recognizes the presupposition as an entailment , even when the trigger is embedded under an entailment canceling operator like negation .",
"bow and infersent show weaker evidence of pragmatic reasoning .",
"we conclude that nli training encourages models to learn some , but not all , pragmatic inferences ."
] |
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ACL
|
Leveraging Task Transferability to Meta-learning for Clinical Section Classification with Limited Data
|
Identifying sections is one of the critical components of understanding medical information from unstructured clinical notes and developing assistive technologies for clinical note-writing tasks. Most state-of-the-art text classification systems require thousands of in-domain text data to achieve high performance. However, collecting in-domain and recent clinical note data with section labels is challenging given the high level of privacy and sensitivity. The present paper proposes an algorithmic way to improve the task transferability of meta-learning-based text classification in order to address the issue of low-resource target data. Specifically, we explore how to make the best use of the source dataset and propose a unique task transferability measure named Normalized Negative Conditional Entropy (NNCE). Leveraging the NNCE, we develop strategies for selecting clinical categories and sections from source task data to boost cross-domain meta-learning accuracy. Experimental results show that our task selection strategies improve section classification accuracy significantly compared to meta-learning algorithms.
|
6bab1cf097070e6d457c9c8fd0e74e57
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"leveraging the nnce , we develop strategies for selecting clinical categories and sections from source task data to boost cross - domain meta - learning accuracy .",
"experimental results show that our task selection strategies improve section classification accuracy significantly compared to meta - learning algorithms ."
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ACL
|
Generate, Delete and Rewrite: A Three-Stage Framework for Improving Persona Consistency of Dialogue Generation
|
Maintaining a consistent personality in conversations is quite natural for human beings, but is still a non-trivial task for machines. The persona-based dialogue generation task is thus introduced to tackle the personality-inconsistent problem by incorporating explicit persona text into dialogue generation models. Despite the success of existing persona-based models on generating human-like responses, their one-stage decoding framework can hardly avoid the generation of inconsistent persona words. In this work, we introduce a three-stage framework that employs a generate-delete-rewrite mechanism to delete inconsistent words from a generated response prototype and further rewrite it to a personality-consistent one. We carry out evaluations by both human and automatic metrics. Experiments on the Persona-Chat dataset show that our approach achieves good performance.
|
71ff0f02bc14a28822f0cdf6c508aae2
| 2,020
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"the persona - based dialogue generation task is thus introduced to tackle the personality - inconsistent problem by incorporating explicit persona text into dialogue generation models .",
"despite the success of existing persona - based models on generating human - like responses , their one - stage decoding framework can hardly avoid the generation of inconsistent persona words .",
"in this work , we introduce a three - stage framework that employs a generate - delete - rewrite mechanism to delete inconsistent words from a generated response prototype and further rewrite it to a personality - consistent one .",
"we carry out evaluations by both human and automatic metrics .",
"experiments on the persona - chat dataset show that our approach achieves good performance ."
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ACL
|
An In-depth Study on Internal Structure of Chinese Words
|
Unlike English letters, Chinese characters have rich and specific meanings. Usually, the meaning of a word can be derived from its constituent characters in some way. Several previous works on syntactic parsing propose to annotate shallow word-internal structures for better utilizing character-level information. This work proposes to model the deep internal structures of Chinese words as dependency trees with 11 labels for distinguishing syntactic relationships. First, based on newly compiled annotation guidelines, we manually annotate a word-internal structure treebank (WIST) consisting of over 30K multi-char words from Chinese Penn Treebank. To guarantee quality, each word is independently annotated by two annotators and inconsistencies are handled by a third senior annotator. Second, we present detailed and interesting analysis on WIST to reveal insights on Chinese word formation. Third, we propose word-internal structure parsing as a new task, and conduct benchmark experiments using a competitive dependency parser. Finally, we present two simple ways to encode word-internal structures, leading to promising gains on the sentence-level syntactic parsing task.
|
636dd0c8ece0788d40d37b9f500026d8
| 2,021
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"this work proposes to model the deep internal structures of chinese words as dependency trees with 11 labels for distinguishing syntactic relationships .",
"first , based on newly compiled annotation guidelines , we manually annotate a word - internal structure treebank ( wist ) consisting of over 30k multi - char words from chinese penn treebank .",
"to guarantee quality , each word is independently annotated by two annotators and inconsistencies are handled by a third senior annotator .",
"second , we present detailed and interesting analysis on wist to reveal insights on chinese word formation .",
"third , we propose word - internal structure parsing as a new task , and conduct benchmark experiments using a competitive dependency parser .",
"finally , we present two simple ways to encode word - internal structures , leading to promising gains on the sentence - level syntactic parsing task ."
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ACL
|
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking
|
Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset. In this paper, we propose to use curriculum learning (CL) to better leverage both the curriculum structure and schema structure for task-oriented dialogs. Specifically, we propose a model-agnostic framework called Schema-aware Curriculum Learning for Dialog State Tracking (SaCLog), which consists of a preview module that pre-trains a DST model with schema information, a curriculum module that optimizes the model with CL, and a review module that augments mispredicted data to reinforce the CL training. We show that our proposed approach improves DST performance over both a transformer-based and RNN-based DST model (TripPy and TRADE) and achieves new state-of-the-art results on WOZ2.0 and MultiWOZ2.1.
|
a0fd29c17984ed8d2e2b7f86831cb0a4
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"we show that our proposed approach improves dst performance over both a transformer - based and rnn - based dst model ( trippy and trade ) and achieves new state - of - the - art results on woz2 . 0 and multiwoz2 . 1 ."
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ACL
|
Self-Attentional Models for Lattice Inputs
|
Lattices are an efficient and effective method to encode ambiguity of upstream systems in natural language processing tasks, for example to compactly capture multiple speech recognition hypotheses, or to represent multiple linguistic analyses. Previous work has extended recurrent neural networks to model lattice inputs and achieved improvements in various tasks, but these models suffer from very slow computation speeds. This paper extends the recently proposed paradigm of self-attention to handle lattice inputs. Self-attention is a sequence modeling technique that relates inputs to one another by computing pairwise similarities and has gained popularity for both its strong results and its computational efficiency. To extend such models to handle lattices, we introduce probabilistic reachability masks that incorporate lattice structure into the model and support lattice scores if available. We also propose a method for adapting positional embeddings to lattice structures. We apply the proposed model to a speech translation task and find that it outperforms all examined baselines while being much faster to compute than previous neural lattice models during both training and inference.
|
8e057b24ffe8ed4a5448b19bb7b9c2bf
| 2,019
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ACL
|
Joint Effects of Context and User History for Predicting Online Conversation Re-entries
|
As the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help users better engage with each other online, we study a challenging problem of re-entry prediction foreseeing whether a user will come back to a conversation they once participated in. We hypothesize that both the context of the ongoing conversations and the users’ previous chatting history will affect their continued interests in future engagement. Specifically, we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting history jointly result in their re-entry behavior. We experiment with two large-scale datasets collected from Twitter and Reddit. Results show that our proposed framework with bi-attention achieves an F1 score of 61.1 on Twitter conversations, outperforming the state-of-the-art methods from previous work.
|
54dc18f3c81976ab42c7f5f4bd591db4
| 2,019
|
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"we hypothesize that both the context of the ongoing conversations and the users ’ previous chatting history will affect their continued interests in future engagement .",
"specifically , we propose a neural framework with three main layers , each modeling context , user history , and interactions between them , to explore how the conversation context and user chatting history jointly result in their re - entry behavior .",
"we experiment with two large - scale datasets collected from twitter and reddit .",
"results show that our proposed framework with bi - attention achieves an f1 score of 61 . 1 on twitter conversations , outperforming the state - of - the - art methods from previous work ."
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ACL
|
Probing for Predicate Argument Structures in Pretrained Language Models
|
Thanks to the effectiveness and wide availability of modern pretrained language models (PLMs), recently proposed approaches have achieved remarkable results in dependency- and span-based, multilingual and cross-lingual Semantic Role Labeling (SRL). These results have prompted researchers to investigate the inner workings of modern PLMs with the aim of understanding how, where, and to what extent they encode information about SRL. In this paper, we follow this line of research and probe for predicate argument structures in PLMs. Our study shows that PLMs do encode semantic structures directly into the contextualized representation of a predicate, and also provides insights into the correlation between predicate senses and their structures, the degree of transferability between nominal and verbal structures, and how such structures are encoded across languages. Finally, we look at the practical implications of such insights and demonstrate the benefits of embedding predicate argument structure information into an SRL model.
|
81cb7fa52062f9d17d9e93a1e4567dec
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ACL
|
Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals
|
Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, and the travel time for booking a flight. However, in many scenarios, limited by experience and knowledge, users may know what they need, but still struggle to figure out clear and specific goals by determining all the necessary slots. In this paper, we identify this challenge, and make a step forward by collecting a new human-to-human mixed-type dialog corpus. It contains 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains. Within each session, an agent first provides user-goal-related knowledge to help figure out clear and specific goals, and then help achieve them. Furthermore, we propose a mixed-type dialog model with a novel Prompt-based continual learning mechanism. Specifically, the mechanism enables the model to continually strengthen its ability on any specific type by utilizing existing dialog corpora effectively.
|
e6819b3ce223923478bb9d3b63e830a6
| 2,022
|
[
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"however , in many scenarios , limited by experience and knowledge , users may know what they need , but still struggle to figure out clear and specific goals by determining all the necessary slots .",
"in this paper , we identify this challenge , and make a step forward by collecting a new human - to - human mixed - type dialog corpus .",
"it contains 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains .",
"within each session , an agent first provides user - goal - related knowledge to help figure out clear and specific goals , and then help achieve them .",
"furthermore , we propose a mixed - type dialog model with a novel prompt - based continual learning mechanism .",
"specifically , the mechanism enables the model to continually strengthen its ability on any specific type by utilizing existing dialog corpora effectively ."
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ACL
|
DVD: A Diagnostic Dataset for Multi-step Reasoning in Video Grounded Dialogue
|
A video-grounded dialogue system is required to understand both dialogue, which contains semantic dependencies from turn to turn, and video, which contains visual cues of spatial and temporal scene variations. Building such dialogue systems is a challenging problem, involving various reasoning types on both visual and language inputs. Existing benchmarks do not have enough annotations to thoroughly analyze dialogue systems and understand their capabilities and limitations in isolation. These benchmarks are also not explicitly designed to minimise biases that models can exploit without actual reasoning. To address these limitations, in this paper, we present DVD, a Diagnostic Dataset for Video-grounded Dialogue. The dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio-temporal space of video. Dialogues are synthesized over multiple question turns, each of which is injected with a set of cross-turn semantic relationships. We use DVD to analyze existing approaches, providing interesting insights into their abilities and limitations. In total, DVD is built from 11k CATER synthetic videos and contains 10 instances of 10-round dialogues for each video, resulting in more than 100k dialogues and 1M question-answer pairs. Our code and dataset are publicly available.
|
d45cd0ddedda4f5e033a5ce54cd0afb9
| 2,021
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"a video - grounded dialogue system is required to understand both dialogue , which contains semantic dependencies from turn to turn , and video , which contains visual cues of spatial and temporal scene variations .",
"building such dialogue systems is a challenging problem , involving various reasoning types on both visual and language inputs .",
"existing benchmarks do not have enough annotations to thoroughly analyze dialogue systems and understand their capabilities and limitations in isolation .",
"these benchmarks are also not explicitly designed to minimise biases that models can exploit without actual reasoning .",
"to address these limitations , in this paper , we present dvd , a diagnostic dataset for video - grounded dialogue .",
"the dataset is designed to contain minimal biases and has detailed annotations for the different types of reasoning over the spatio - temporal space of video .",
"dialogues are synthesized over multiple question turns , each of which is injected with a set of cross - turn semantic relationships .",
"we use dvd to analyze existing approaches , providing interesting insights into their abilities and limitations .",
"in total , dvd is built from 11k cater synthetic videos and contains 10 instances of 10 - round dialogues for each video , resulting in more than 100k dialogues and 1m question - answer pairs .",
"our code and dataset are publicly available ."
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ACL
|
MATE-KD: Masked Adversarial TExt, a Companion to Knowledge Distillation
|
The advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks has scaled with size, compression techniques such as knowledge distillation have been key in making them practical. We present MATE-KD, a novel text-based adversarial training algorithm which improves the performance of knowledge distillation. MATE-KD first trains a masked language model-based generator to perturb text by maximizing the divergence between teacher and student logits. Then using knowledge distillation a student is trained on both the original and the perturbed training samples. We evaluate our algorithm, using BERT-based models, on the GLUE benchmark and demonstrate that MATE-KD outperforms competitive adversarial learning and data augmentation baselines. On the GLUE test set our 6 layer RoBERTa based model outperforms BERT-large.
|
bcf2a5086a3b7ab9ae680289f38dad5f
| 2,021
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[
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"we present mate - kd , a novel text - based adversarial training algorithm which improves the performance of knowledge distillation .",
"mate - kd first trains a masked language model - based generator to perturb text by maximizing the divergence between teacher and student logits .",
"then using knowledge distillation a student is trained on both the original and the perturbed training samples .",
"we evaluate our algorithm , using bert - based models , on the glue benchmark and demonstrate that mate - kd outperforms competitive adversarial learning and data augmentation baselines .",
"on the glue test set our 6 layer roberta based model outperforms bert - large ."
] |
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ACL
|
An Automated Framework for Fast Cognate Detection and Bayesian Phylogenetic Inference in Computational Historical Linguistics
|
We present a fully automated workflow for phylogenetic reconstruction on large datasets, consisting of two novel methods, one for fast detection of cognates and one for fast Bayesian phylogenetic inference. Our results show that the methods take less than a few minutes to process language families that have so far required large amounts of time and computational power. Moreover, the cognates and the trees inferred from the method are quite close, both to gold standard cognate judgments and to expert language family trees. Given its speed and ease of application, our framework is specifically useful for the exploration of very large datasets in historical linguistics.
|
db2fff29a55036937a41cdace0266be9
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ACL
|
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization
|
Despite recent progress in abstractive summarization, systems still suffer from faithfulness errors. While prior work has proposed models that improve faithfulness, it is unclear whether the improvement comes from an increased level of extractiveness of the model outputs as one naive way to improve faithfulness is to make summarization models more extractive. In this work, we present a framework for evaluating the effective faithfulness of summarization systems, by generating a faithfulness-abstractiveness trade-off curve that serves as a control at different operating points on the abstractiveness spectrum. We then show that the Maximum Likelihood Estimation (MLE) baseline as well as recently proposed methods for improving faithfulness, fail to consistently improve over the control at the same level of abstractiveness. Finally, we learn a selector to identify the most faithful and abstractive summary for a given document, and show that this system can attain higher faithfulness scores in human evaluations while being more abstractive than the baseline system on two datasets. Moreover, we show that our system is able to achieve a better faithfulness-abstractiveness trade-off than the control at the same level of abstractiveness.
|
959fbecee82a093efd41a9a4608a4728
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ACL
|
Enhancing the generalization for Intent Classification and Out-of-Domain Detection in SLU
|
Intent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances has a critical effect in practical use. Recent works have shown that using extra data and labels can improve the OOD detection performance, yet it could be costly to collect such data. This paper proposes to train a model with only IND data while supporting both IND intent classification and OOD detection. Our method designs a novel domain-regularized module (DRM) to reduce the overconfident phenomenon of a vanilla classifier, achieving a better generalization in both cases. Besides, DRM can be used as a drop-in replacement for the last layer in any neural network-based intent classifier, providing a low-cost strategy for a significant improvement. The evaluation on four datasets shows that our method built on BERT and RoBERTa models achieves state-of-the-art performance against existing approaches and the strong baselines we created for the comparisons.
|
bbe87249393bc09725f2b0dcfda04997
| 2,021
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"intent classification is a major task in spoken language understanding ( slu ) .",
"since most models are built with pre - collected in - domain ( ind ) training utterances , their ability to detect unsupported out - of - domain ( ood ) utterances has a critical effect in practical use .",
"recent works have shown that using extra data and labels can improve the ood detection performance , yet it could be costly to collect such data .",
"this paper proposes to train a model with only ind data while supporting both ind intent classification and ood detection .",
"our method designs a novel domain - regularized module ( drm ) to reduce the overconfident phenomenon of a vanilla classifier , achieving a better generalization in both cases .",
"besides , drm can be used as a drop - in replacement for the last layer in any neural network - based intent classifier , providing a low - cost strategy for a significant improvement .",
"the evaluation on four datasets shows that our method built on bert and roberta models achieves state - of - the - art performance against existing approaches and the strong baselines we created for the comparisons ."
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] |
ACL
|
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
|
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based Data Augmentation model (PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable vectors) in the frozen Pre-trained Language Models (PLMs). This avoids human effort in collecting unlabeled in-domain data and maintains the quality of generated synthetic data. In addition, PromDA generates synthetic data via two different views and filters out the low-quality data using NLU models. Experiments on four benchmarks show that synthetic data produced by PromDA successfully boost up the performance of NLU models which consistently outperform several competitive baseline models, including a state-of-the-art semi-supervised model using unlabeled in-domain data. The synthetic data from PromDA are also complementary with unlabeled in-domain data. The NLU models can be further improved when they are combined for training.
|
04e5995f7999d5daad821408248f8262
| 2,022
|
[
"this paper focuses on the data augmentation for low - resource natural language understanding ( nlu ) tasks .",
"we propose prompt - based data augmentation model ( promda ) which only trains small - scale soft prompt ( i . e . , a set of trainable vectors ) in the frozen pre - trained language models ( plms ) .",
"this avoids human effort in collecting unlabeled in - domain data and maintains the quality of generated synthetic data .",
"in addition , promda generates synthetic data via two different views and filters out the low - quality data using nlu models .",
"experiments on four benchmarks show that synthetic data produced by promda successfully boost up the performance of nlu models which consistently outperform several competitive baseline models , including a state - of - the - art semi - supervised model using unlabeled in - domain data .",
"the synthetic data from promda are also complementary with unlabeled in - domain data .",
"the nlu models can be further improved when they are combined for training ."
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ACL
|
Analyzing the Limitations of Cross-lingual Word Embedding Mappings
|
Recent research in cross-lingual word embeddings has almost exclusively focused on offline methods, which independently train word embeddings in different languages and map them to a shared space through linear transformations. While several authors have questioned the underlying isomorphism assumption, which states that word embeddings in different languages have approximately the same structure, it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning cross-lingual embeddings. So as to answer this question, we experiment with parallel corpora, which allows us to compare offline mapping to an extension of skip-gram that jointly learns both embedding spaces. We observe that, under these ideal conditions, joint learning yields to more isomorphic embeddings, is less sensitive to hubness, and obtains stronger results in bilingual lexicon induction. We thus conclude that current mapping methods do have strong limitations, calling for further research to jointly learn cross-lingual embeddings with a weaker cross-lingual signal.
|
c84823d8450a619b600d844943a96c1e
| 2,019
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"while several authors have questioned the underlying isomorphism assumption , which states that word embeddings in different languages have approximately the same structure , it is not clear whether this is an inherent limitation of mapping approaches or a more general issue when learning cross - lingual embeddings .",
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"we thus conclude that current mapping methods do have strong limitations , calling for further research to jointly learn cross - lingual embeddings with a weaker cross - lingual signal ."
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ACL
|
Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks
|
Syntactic information, especially dependency trees, has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities. However, most existing studies suffer from the noise in the dependency trees, especially when they are automatically generated, so that intensively leveraging dependency information may introduce confusions to relation classification and necessary pruning is of great importance in this task. In this paper, we propose a dependency-driven approach for relation extraction with attentive graph convolutional networks (A-GCN). In this approach, an attention mechanism upon graph convolutional networks is applied to different contextual words in the dependency tree obtained from an off-the-shelf dependency parser, to distinguish the importance of different word dependencies. Consider that dependency types among words also contain important contextual guidance, which is potentially helpful for relation extraction, we also include the type information in A-GCN modeling. Experimental results on two English benchmark datasets demonstrate the effectiveness of our A-GCN, which outperforms previous studies and achieves state-of-the-art performance on both datasets.
|
09e8a58fe50453a2401747d5e9c40e18
| 2,021
|
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"syntactic information , especially dependency trees , has been widely used by existing studies to improve relation extraction with better semantic guidance for analyzing the context information associated with the given entities .",
"however , most existing studies suffer from the noise in the dependency trees , especially when they are automatically generated , so that intensively leveraging dependency information may introduce confusions to relation classification and necessary pruning is of great importance in this task .",
"in this paper , we propose a dependency - driven approach for relation extraction with attentive graph convolutional networks ( a - gcn ) .",
"in this approach , an attention mechanism upon graph convolutional networks is applied to different contextual words in the dependency tree obtained from an off - the - shelf dependency parser , to distinguish the importance of different word dependencies .",
"consider that dependency types among words also contain important contextual guidance , which is potentially helpful for relation extraction , we also include the type information in a - gcn modeling .",
"experimental results on two english benchmark datasets demonstrate the effectiveness of our a - gcn , which outperforms previous studies and achieves state - of - the - art performance on both datasets ."
] |
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ACL
|
Joint Chinese Word Segmentation and Part-of-speech Tagging via Two-way Attentions of Auto-analyzed Knowledge
|
Chinese word segmentation (CWS) and part-of-speech (POS) tagging are important fundamental tasks for Chinese language processing, where joint learning of them is an effective one-step solution for both tasks. Previous studies for joint CWS and POS tagging mainly follow the character-based tagging paradigm with introducing contextual information such as n-gram features or sentential representations from recurrent neural models. However, for many cases, the joint tagging needs not only modeling from context features but also knowledge attached to them (e.g., syntactic relations among words); limited efforts have been made by existing research to meet such needs. In this paper, we propose a neural model named TwASP for joint CWS and POS tagging following the character-based sequence labeling paradigm, where a two-way attention mechanism is used to incorporate both context feature and their corresponding syntactic knowledge for each input character. Particularly, we use existing language processing toolkits to obtain the auto-analyzed syntactic knowledge for the context, and the proposed attention module can learn and benefit from them although their quality may not be perfect. Our experiments illustrate the effectiveness of the two-way attentions for joint CWS and POS tagging, where state-of-the-art performance is achieved on five benchmark datasets.
|
9609778ad9e5f0ef4d2c7c494df6a6dc
| 2,020
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"however , for many cases , the joint tagging needs not only modeling from context features but also knowledge attached to them ( e . g . , syntactic relations among words ) ; limited efforts have been made by existing research to meet such needs .",
"in this paper , we propose a neural model named twasp for joint cws and pos tagging following the character - based sequence labeling paradigm , where a two - way attention mechanism is used to incorporate both context feature and their corresponding syntactic knowledge for each input character .",
"particularly , we use existing language processing toolkits to obtain the auto - analyzed syntactic knowledge for the context , and the proposed attention module can learn and benefit from them although their quality may not be perfect .",
"our experiments illustrate the effectiveness of the two - way attentions for joint cws and pos tagging , where state - of - the - art performance is achieved on five benchmark datasets ."
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ACL
|
RankQA: Neural Question Answering with Answer Re-Ranking
|
The conventional paradigm in neural question answering (QA) for narrative content is limited to a two-stage process: first, relevant text passages are retrieved and, subsequently, a neural network for machine comprehension extracts the likeliest answer. However, both stages are largely isolated in the status quo and, hence, information from the two phases is never properly fused. In contrast, this work proposes RankQA: RankQA extends the conventional two-stage process in neural QA with a third stage that performs an additional answer re-ranking. The re-ranking leverages different features that are directly extracted from the QA pipeline, i.e., a combination of retrieval and comprehension features. While our intentionally simple design allows for an efficient, data-sparse estimation, it nevertheless outperforms more complex QA systems by a significant margin: in fact, RankQA achieves state-of-the-art performance on 3 out of 4 benchmark datasets. Furthermore, its performance is especially superior in settings where the size of the corpus is dynamic. Here the answer re-ranking provides an effective remedy against the underlying noise-information trade-off due to a variable corpus size. As a consequence, RankQA represents a novel, powerful, and thus challenging baseline for future research in content-based QA.
|
864e901c9c8268c1e32b4e85b4cdda05
| 2,019
|
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"however , both stages are largely isolated in the status quo and , hence , information from the two phases is never properly fused .",
"in contrast , this work proposes rankqa : rankqa extends the conventional two - stage process in neural qa with a third stage that performs an additional answer re - ranking .",
"the re - ranking leverages different features that are directly extracted from the qa pipeline , i . e . , a combination of retrieval and comprehension features .",
"while our intentionally simple design allows for an efficient , data - sparse estimation , it nevertheless outperforms more complex qa systems by a significant margin : in fact , rankqa achieves state - of - the - art performance on 3 out of 4 benchmark datasets .",
"furthermore , its performance is especially superior in settings where the size of the corpus is dynamic .",
"here the answer re - ranking provides an effective remedy against the underlying noise - information trade - off due to a variable corpus size .",
"as a consequence , rankqa represents a novel , powerful , and thus challenging baseline for future research in content - based qa ."
] |
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ACL
|
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification
|
Complex word identification (CWI) is a cornerstone process towards proper text simplification. CWI is highly dependent on context, whereas its difficulty is augmented by the scarcity of available datasets which vary greatly in terms of domains and languages. As such, it becomes increasingly more difficult to develop a robust model that generalizes across a wide array of input examples. In this paper, we propose a novel training technique for the CWI task based on domain adaptation to improve the target character and context representations. This technique addresses the problem of working with multiple domains, inasmuch as it creates a way of smoothing the differences between the explored datasets. Moreover, we also propose a similar auxiliary task, namely text simplification, that can be used to complement lexical complexity prediction. Our model obtains a boost of up to 2.42% in terms of Pearson Correlation Coefficients in contrast to vanilla training techniques, when considering the CompLex from the Lexical Complexity Prediction 2021 dataset. At the same time, we obtain an increase of 3% in Pearson scores, while considering a cross-lingual setup relying on the Complex Word Identification 2018 dataset. In addition, our model yields state-of-the-art results in terms of Mean Absolute Error.
|
0e0d6cc75f98e0e32960341f2f384171
| 2,022
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"this technique addresses the problem of working with multiple domains , inasmuch as it creates a way of smoothing the differences between the explored datasets .",
"moreover , we also propose a similar auxiliary task , namely text simplification , that can be used to complement lexical complexity prediction .",
"our model obtains a boost of up to 2 . 42 % in terms of pearson correlation coefficients in contrast to vanilla training techniques , when considering the complex from the lexical complexity prediction 2021 dataset .",
"at the same time , we obtain an increase of 3 % in pearson scores , while considering a cross - lingual setup relying on the complex word identification 2018 dataset .",
"in addition , our model yields state - of - the - art results in terms of mean absolute error ."
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ACL
|
Substructure Distribution Projection for Zero-Shot Cross-Lingual Dependency Parsing
|
We present substructure distribution projection (SubDP), a technique that projects a distribution over structures in one domain to another, by projecting substructure distributions separately. Models for the target domain can then be trained, using the projected distributions as soft silver labels. We evaluate SubDP on zero shot cross-lingual dependency parsing, taking dependency arcs as substructures: we project the predicted dependency arc distributions in the source language(s) to target language(s), and train a target language parser on the resulting distributions. Given an English tree bank as the only source of human supervision, SubDP achieves better unlabeled attachment score than all prior work on the Universal Dependencies v2.2 (Nivre et al., 2020) test set across eight diverse target languages, as well as the best labeled attachment score on six languages. In addition, SubDP improves zero shot cross-lingual dependency parsing with very few (e.g., 50) supervised bitext pairs, across a broader range of target languages.
|
a11d23df083ec881504fcaf35594405c
| 2,022
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"given an english tree bank as the only source of human supervision , subdp achieves better unlabeled attachment score than all prior work on the universal dependencies v2 . 2 ( nivre et al . , 2020 ) test set across eight diverse target languages , as well as the best labeled attachment score on six languages .",
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ACL
|
How can NLP Help Revitalize Endangered Languages? A Case Study and Roadmap for the Cherokee Language
|
More than 43% of the languages spoken in the world are endangered, and language loss currently occurs at an accelerated rate because of globalization and neocolonialism. Saving and revitalizing endangered languages has become very important for maintaining the cultural diversity on our planet. In this work, we focus on discussing how NLP can help revitalize endangered languages. We first suggest three principles that may help NLP practitioners to foster mutual understanding and collaboration with language communities, and we discuss three ways in which NLP can potentially assist in language education. We then take Cherokee, a severely-endangered Native American language, as a case study. After reviewing the language’s history, linguistic features, and existing resources, we (in collaboration with Cherokee community members) arrive at a few meaningful ways NLP practitioners can collaborate with community partners. We suggest two approaches to enrich the Cherokee language’s resources with machine-in-the-loop processing, and discuss several NLP tools that people from the Cherokee community have shown interest in. We hope that our work serves not only to inform the NLP community about Cherokee, but also to provide inspiration for future work on endangered languages in general.
|
6043d6347900bef1481b47f957a5bdf4
| 2,022
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"we hope that our work serves not only to inform the nlp community about cherokee , but also to provide inspiration for future work on endangered languages in general ."
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