id stringclasses 100
values | document stringclasses 100
values | gold_summary stringlengths 76 223 | aspect_selected stringclasses 3
values | prompt_type stringclasses 2
values | prompt stringlengths 2.22k 16.3k | source_dataset stringclasses 1
value | prompt_variant_id int64 0 2 | section stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
2020.acl-main.573 | Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent tr... | Storing histories of examples is shown to be effective for continual relation learning however existing methods suffer from overfitting to memorize a few old memories. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations .... | sobamchan/aclsum | 1 | full_paper |
P19-1252 | In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social... | Existing systems only use limited information from the tweets network to perform occupation classification. | challenge | coverage_first | State the difficulty of the study.
---
Document:
In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follo... | sobamchan/aclsum | 2 | full_paper |
2021.emnlp-main.185 | Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability . Conventional approaches employ the siamese-network for this task , which obtains the sentence embeddings through modeling the context-response semantic relevance by applying a feed-fo... | They propose a dialogue-based contrastive learning approach to learn sentence embeddings from dialogues by modelling semantic matching relationships between the context and response implicitly. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
Learning sentence embeddings from dialogues has drawn increasing attention due to its low annotation cost and high domain adaptability . Conventional approaches employ the siamese-network f... | sobamchan/aclsum | 2 | full_paper |
2021.naacl-main.72 | Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models . Attention redundancy has been observed among attention heads but has not been deeply studied in the literature . Using BERT-base model as an example , this paper provides a comprehensive study on attention redundancy wh... | They perform token and sentence level analysis on redundancy matrices from pre-trained and fine-tuned BERT-base models and further propose a pruning method based on findings. | approach | coverage_first | Give the approach described.
---
Document:
Multi-layer multi-head self-attention mechanism is widely applied in modern neural language models . Attention redundancy has been observed among attention heads but has not been deeply studied in the literature . Using BERT-base model as an example , this paper provides a co... | sobamchan/aclsum | 1 | full_paper |
N13-1083 | We investigate two systems for automatic disfluency detection on English and Mandarin conversational speech data . The first system combines various lexical and prosodic features in a Conditional Random Field model for detecting edit disfluencies . The second system combines acoustic and language model scores for detec... | They evaluate a Conditional Random Field-based edit disfluency detection model and a system which combines acoustic and language model that detects filled pauses in Mandarin. | approach | coverage_first | Summarize the method.
---
Document:
We investigate two systems for automatic disfluency detection on English and Mandarin conversational speech data . The first system combines various lexical and prosodic features in a Conditional Random Field model for detecting edit disfluencies . The second system combines acousti... | sobamchan/aclsum | 0 | full_paper |
2021.naacl-main.150 | A conventional approach to improving the performance of end-to-end speech translation ( E2E-ST ) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition ( ASR ) and neural machine translation ( NMT ) tasks . However , since the input modalities are different ... | Evaluations on autoregressive and non-autoregressive models show that the proposed method improves in both directions and the results are consistent in different model sizes. | outcome | aspect_first | What are the primary outcomes of the study? Give a concise overview. Return only the summary in one sentence.
---
Document:
A conventional approach to improving the performance of end-to-end speech translation ( E2E-ST ) models is to leverage the source transcription via pre-training and joint training with automatic ... | sobamchan/aclsum | 1 | full_paper |
2021.acl-long.420 | We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa . Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage , so that they suffer from discrepancy between the two stages . Such a problem would lead to th... | Existing ways of injecting syntactic knowledge into pretraining models cause discrepancies between pretraining and fine-tuning and require expensive annotation. | challenge | coverage_first | Give the problem in short form.
---
Document:
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa . Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning stage , so that they suffer from discrepancy between t... | sobamchan/aclsum | 1 | full_paper |
P18-1222 | Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hyper-documents . In this paper , we propose a general... | Existing text embedding methods do not take structures of hyper-documents into account losing useful properties for downstream tasks. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , ... | sobamchan/aclsum | 1 | full_paper |
E06-1051 | We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information . We use a combination of kernel functions to integrate two different information sources : ( i ) the whole sentence where the relation appears , and ( ii ) the local contexts aroun... | They propose an approach for entity relation extraction using shallow linguistic information such as tokenization, sentence splitting, Part-of-Speech tagging and lemmatization coupled with kernel functions. | approach | coverage_first | What did the authors do?
---
Document:
We propose an approach for extracting relations between entities from biomedical literature based solely on shallow linguistic information . We use a combination of kernel functions to integrate two different information sources : ( i ) the whole sentence where the relation appea... | sobamchan/aclsum | 2 | full_paper |
P18-1222 | Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hyper-documents . In this paper , we propose a general... | Existing text embedding methods do not take structures of hyper-documents into account losing useful properties for downstream tasks. | challenge | coverage_first | State the difficulty of the study.
---
Document:
Hypertext documents , such as web pages and academic papers , are of great importance in delivering information in our daily life . Although being effective on plain documents , conventional text embedding methods suffer from information loss if directly adapted to hype... | sobamchan/aclsum | 2 | full_paper |
P16-1067 | This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships . For this purpose , we adopt Hidden Markov Model ( HMM ) and construct a sequential... | They propose a HMM-based sequential probabilistic model that captures the dependencies of sequential sentences and their authors coupled with an unsupervised initialization method. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among docume... | sobamchan/aclsum | 2 | full_paper |
N18-1114 | We present a new approach to the design of deep networks for natural language processing ( NLP ) , based on the general technique of Tensor Product Representations ( TPRs ) for encoding and processing symbol structures in distributed neural networks . A network architecture -the Tensor Product Generation Network ( TPGN... | They propose a newly designed model that is based on Tensor Product Representations for encoding and processing words and sentences. | approach | coverage_first | Give the approach described.
---
Document:
We present a new approach to the design of deep networks for natural language processing ( NLP ) , based on the general technique of Tensor Product Representations ( TPRs ) for encoding and processing symbol structures in distributed neural networks . A network architecture -... | sobamchan/aclsum | 1 | full_paper |
2020.acl-main.528 | Recently , many works have tried to augment the performance of Chinese named entity recognition ( NER ) using word lexicons . As a representative , Lattice-LSTM ( Zhang and Yang , 2018 ) has achieved new benchmark results on several public Chinese NER datasets . However , Lattice-LSTM has a complex model architecture .... | Named entity recognition in Chinese requires word segmentation causes errors or character-level model with lexical features that is complex and expensive. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
---
Document:
Recently , many works have tried to augment the performance of Chinese named entity recognition ( NER ) using word lexicons . As a representative , Lattice-LSTM ( ... | sobamchan/aclsum | 0 | full_paper |
E09-1032 | We explore the problem of resolving the second person English pronoun you in multi-party dialogue , using a combination of linguistic and visual features . First , we distinguish generic and referential uses , then we classify the referential uses as either plural or singular , and finally , for the latter cases , we i... | Although the word "you" is frequently used and has several possible meanings, such as reference or generic, it is not well studied yet. | challenge | aspect_first | Extract a short summary of the core issue that the paper targets. Return only the summary in one sentence.
---
Document:
We explore the problem of resolving the second person English pronoun you in multi-party dialogue , using a combination of linguistic and visual features . First , we distinguish generic and referen... | sobamchan/aclsum | 2 | full_paper |
D09-1115 | Current system combination methods usually use confusion networks to find consensus translations among different systems . Requiring one-to-one mappings between the words in candidate translations , confusion networks have difficulty in handling more general situations in which several words are connected to another se... | System combination methods based on confusion networks only allow word level 1-to-1 mappings, and some workarounds cause another type of problem such as degeneration. | challenge | aspect_first | Extract a short summary of the core issue that the paper targets. Return only the summary in one sentence.
---
Document:
Current system combination methods usually use confusion networks to find consensus translations among different systems . Requiring one-to-one mappings between the words in candidate translations ,... | sobamchan/aclsum | 2 | full_paper |
N19-1233 | Generative Adversarial Networks ( GANs ) are a promising approach for text generation that , unlike traditional language models ( LM ) , does not suffer from the problem of " exposure bias " . However , A major hurdle for understanding the potential of GANs for text generation is the lack of a clear evaluation metric .... | They propose a way to approximate distributions from GAN-based models' outputs so that they can be evaluated as standard language models. | approach | coverage_first | What did the authors do?
---
Document:
Generative Adversarial Networks ( GANs ) are a promising approach for text generation that , unlike traditional language models ( LM ) , does not suffer from the problem of " exposure bias " . However , A major hurdle for understanding the potential of GANs for text generation is... | sobamchan/aclsum | 2 | full_paper |
N09-1072 | Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue systems and social computing . We describe a system for detecting elements of interactional style : whether a speaker is awkward , friendly , or flirtatious . We create and use a new spoken corpus of 991 4-minut... | Methods to extract social meanings such as engagement from speech remain unknown while it is important in sociolinguistics and to develop socially aware computing systems. | challenge | coverage_first | State the difficulty of the study.
---
Document:
Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue systems and social computing . We describe a system for detecting elements of interactional style : whether a speaker is awkward , friendly , or flirtatious . We... | sobamchan/aclsum | 2 | full_paper |
2020.acl-main.443 | There is an increasing interest in studying natural language and computer code together , as large corpora of programming texts become readily available on the Internet . For example , StackOverflow currently has over 15 million programming related questions written by 8.5 million users . Meanwhile , there is still a l... | The proposed model outperforms BiLSTM-CRF and fine-tuned BERT models by achieving a 79.10 F1 score for code and named entity recognition on their dataset which | outcome | aspect_first | Extract a short summary of the paperโs results and conclusions. Return only the summary in one sentence.
---
Document:
There is an increasing interest in studying natural language and computer code together , as large corpora of programming texts become readily available on the Internet . For example , StackOverflow c... | sobamchan/aclsum | 2 | full_paper |
D18-1065 | In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy , accurate , and efficient attention mechanism for sequence to sequence learning . The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention . O... | Softmax attention models are popular because of their differentiable and easy to implement nature while hard attention models outperform them when successfully trained. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
---
Document:
In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy , accurate , and efficient attention mecha... | sobamchan/aclsum | 0 | full_paper |
2020.acl-main.573 | Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent tr... | Storing histories of examples is shown to be effective for continual relation learning however existing methods suffer from overfitting to memorize a few old memories. | challenge | coverage_first | State the difficulty of the study.
---
Document:
Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in e... | sobamchan/aclsum | 2 | full_paper |
D09-1042 | This paper presents an effective method for generating natural language sentences from their underlying meaning representations . The method is built on top of a hybrid tree representation that jointly encodes both the meaning representation as well as the natural language in a tree structure . By using a tree conditio... | They propose a phrase-level tree conditional random field that uses a hybrid tree of a meaning representation for the text generation model. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
This paper presents an effective method for generating natural language sentences from their underlying meaning representations . The method is built on top of a hybrid tree representation ... | sobamchan/aclsum | 2 | full_paper |
P19-1081 | We study a conversational reasoning model that strategically traverses through a largescale common fact knowledge graph ( KG ) to introduce engaging and contextually diverse entities and attributes . For this study , we collect a new Open-ended Dialog โ KG parallel corpus called OpenDialKG , where each utterance from 1... | Using a large knowledge base for dialogue systems is intractable or not scalable which calls for methods that prune search space for entities. | challenge | coverage_first | What issue is this paper about?
---
Document:
We study a conversational reasoning model that strategically traverses through a largescale common fact knowledge graph ( KG ) to introduce engaging and contextually diverse entities and attributes . For this study , we collect a new Open-ended Dialog โ KG parallel corpus ... | sobamchan/aclsum | 0 | full_paper |
E14-1026 | We present a simple preordering approach for machine translation based on a featurerich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not . Given the pair-wise children regression scores we conduct an efficient depth-first branch-and-bound ... | Preordering methods for machine translation systems that involve little or no human assistance, run on limited computational resources and use linguistic analysis tools are required. | challenge | coverage_first | Give the problem in short form.
---
Document:
We present a simple preordering approach for machine translation based on a featurerich logistic regression model to predict whether two children of the same node in the source-side parse tree should be swapped or not . Given the pair-wise children regression scores we con... | sobamchan/aclsum | 1 | full_paper |
P10-1077 | Prior use of machine learning in genre classification used a list of labels as classification categories . However , genre classes are often organised into hierarchies , e.g. , covering the subgenres of fiction . In this paper we present a method of using the hierarchy of labels to improve the classification accuracy .... | The proposed model outperforms non-hierarchical models on only one corpus and they discuss that it may be due to insufficient depth or inbalance of hierarchies. | outcome | coverage_first | Summarize the results.
---
Document:
Prior use of machine learning in genre classification used a list of labels as classification categories . However , genre classes are often organised into hierarchies , e.g. , covering the subgenres of fiction . In this paper we present a method of using the hierarchy of labels to... | sobamchan/aclsum | 0 | full_paper |
2021.emnlp-main.66 | This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but if someone says " A man is walking a chicken in the park , " it is novel . Given a set of natural language descriptions of normal s... | The proposed model outperforms 11 baseline models on the created dataset from an image caption dataset for the proposed task by large margins. | outcome | aspect_first | Summarize the main results or outcomes reported by the authors. Return only the summary in one sentence.
---
Document:
This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but if someone ... | sobamchan/aclsum | 0 | full_paper |
2021.emnlp-main.66 | This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but if someone says " A man is walking a chicken in the park , " it is novel . Given a set of natural language descriptions of normal s... | The proposed model outperforms 11 baseline models on the created dataset from an image caption dataset for the proposed task by large margins. | outcome | coverage_first | Summarize the results.
---
Document:
This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but if someone says " A man is walking a chicken in the park , " it is novel . Given a set of nat... | sobamchan/aclsum | 0 | full_paper |
N16-1103 | Universal schema builds a knowledge base ( KB ) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns observed in raw text . In most previous applications of universal schema , each textual pattern is represented as a single embedding , preventing generalization to... | Existing approaches to incorporate universal schemas for automatic knowledge base construction has limitation in generalization to unseen inputs from training time. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
Universal schema builds a knowledge base ( KB ) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns observed in raw text ... | sobamchan/aclsum | 1 | full_paper |
D09-1072 | We propose a new model for unsupervised POS tagging based on linguistic distinctions between open and closed-class items . Exploiting notions from current linguistic theory , the system uses far less information than previous systems , far simpler computational methods , and far sparser descriptions in learning context... | They propose to first identify functional syntactic contexts and then use them to make predictions for POS tagging. | approach | coverage_first | What did the authors do?
---
Document:
We propose a new model for unsupervised POS tagging based on linguistic distinctions between open and closed-class items . Exploiting notions from current linguistic theory , the system uses far less information than previous systems , far simpler computational methods , and far ... | sobamchan/aclsum | 2 | full_paper |
P16-1089 | We present the Siamese Continuous Bag of Words ( Siamese CBOW ) model , a neural network for efficient estimation of highquality sentence embeddings . Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings . However , word embeddings... | They propose to train word embeddings directly for the purpose of being averaged by predicting sounding sentences from a sentence representation using unlabeled data. | approach | coverage_first | What did the authors do?
---
Document:
We present the Siamese Continuous Bag of Words ( Siamese CBOW ) model , a neural network for efficient estimation of highquality sentence embeddings . Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentenc... | sobamchan/aclsum | 2 | full_paper |
P12-1013 | Learning entailment rules is fundamental in many semantic-inference applications and has been an active field of research in recent years . In this paper we address the problem of learning transitive graphs that describe entailment rules between predicates ( termed entailment graphs ) . We first identify that entailmen... | Current inefficient algorithms aim to obtain entailment rules for semantic inference hindering the use of large resources. | challenge | coverage_first | State the difficulty of the study.
---
Document:
Learning entailment rules is fundamental in many semantic-inference applications and has been an active field of research in recent years . In this paper we address the problem of learning transitive graphs that describe entailment rules between predicates ( termed enta... | sobamchan/aclsum | 2 | full_paper |
N07-1011 | Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases . In this paper , we propose a machine learning method that enables features over sets of noun phrases , resulting in a first-order probabilistic model for coreference . We outline a set of approximations that make t... | Evaluation on the ACE coreference dataset, the proposed method achieves a 45% error reduction over a previous method. | outcome | coverage_first | Describe the conclusions briefly.
---
Document:
Traditional noun phrase coreference resolution systems represent features only of pairs of noun phrases . In this paper , we propose a machine learning method that enables features over sets of noun phrases , resulting in a first-order probabilistic model for coreference... | sobamchan/aclsum | 2 | full_paper |
2020.emnlp-main.384 | Word embedding models are typically able to capture the semantics of words via the distributional hypothesis , but fail to capture the numerical properties of numbers that appear in a text . This leads to problems with numerical reasoning involving tasks such as question answering . We propose a new methodology to assi... | They propose a deterministic technique to learn numerical embeddings where cosine similarity reflects the actual distance and a regularization approach for a contextual setting. | approach | coverage_first | Give the approach described.
---
Document:
Word embedding models are typically able to capture the semantics of words via the distributional hypothesis , but fail to capture the numerical properties of numbers that appear in a text . This leads to problems with numerical reasoning involving tasks such as question answ... | sobamchan/aclsum | 1 | full_paper |
P19-1252 | In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social... | They show that textual feature enables graph neural networks to predict Twitter user occupation well even with a limited amount of training data. | outcome | coverage_first | Describe the conclusions briefly.
---
Document:
In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follow... | sobamchan/aclsum | 2 | full_paper |
D13-1158 | Recent studies on extractive text summarization formulate it as a combinatorial optimization problem such as a Knapsack Problem , a Maximum Coverage Problem or a Budgeted Median Problem . These methods successfully improved summarization quality , but they did not consider the rhetorical relations between the textual u... | Existing optimization-based methods for extractive summarization do not consider the rhetorical relations between textual units leading to generating uncoherent summaries or missing significant textual units. | challenge | coverage_first | State the difficulty of the study.
---
Document:
Recent studies on extractive text summarization formulate it as a combinatorial optimization problem such as a Knapsack Problem , a Maximum Coverage Problem or a Budgeted Median Problem . These methods successfully improved summarization quality , but they did not consi... | sobamchan/aclsum | 2 | full_paper |
P19-1252 | In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social... | Existing systems only use limited information from the tweets network to perform occupation classification. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show th... | sobamchan/aclsum | 1 | full_paper |
P02-1051 | Named entity phrases are some of the most difficult phrases to translate because new phrases can appear from nowhere , and because many are domain specific , not to be found in bilingual dictionaries . We present a novel algorithm for translating named entity phrases using easily obtainable monolingual and bilingual re... | Translating named entities is challenging since they can appear from nowhere, and cannot be found in bilingual dictionaries because they are domain specific. | challenge | coverage_first | State the difficulty of the study.
---
Document:
Named entity phrases are some of the most difficult phrases to translate because new phrases can appear from nowhere , and because many are domain specific , not to be found in bilingual dictionaries . We present a novel algorithm for translating named entity phrases us... | sobamchan/aclsum | 2 | full_paper |
P12-1103 | We propose a novel approach to improve SMT via paraphrase rules which are automatically extracted from the bilingual training data . Without using extra paraphrase resources , we acquire the rules by comparing the source side of the parallel corpus with the target-to-source translations of the target side . Besides the... | Incorporating paraphrases improves statistical machine translation however no works investigate sentence level paraphrases. | challenge | coverage_first | State the difficulty of the study.
---
Document:
We propose a novel approach to improve SMT via paraphrase rules which are automatically extracted from the bilingual training data . Without using extra paraphrase resources , we acquire the rules by comparing the source side of the parallel corpus with the target-to-so... | sobamchan/aclsum | 2 | full_paper |
P16-1067 | This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships . For this purpose , we adopt Hidden Markov Model ( HMM ) and construct a sequential... | Experiments with artificial and authentic scientific document datasets show that the proposed model outperforms existing methods and also be able to provide confidence scores. | outcome | coverage_first | Describe the conclusions briefly.
---
Document:
This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components . The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships . For this purpose , we adopt Hidde... | sobamchan/aclsum | 2 | full_paper |
D09-1072 | We propose a new model for unsupervised POS tagging based on linguistic distinctions between open and closed-class items . Exploiting notions from current linguistic theory , the system uses far less information than previous systems , far simpler computational methods , and far sparser descriptions in learning context... | Current approaches tackle unsupervised POS tagging as a sequential labelling problem and require a complete knowledge of the lexicon. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
We propose a new model for unsupervised POS tagging based on linguistic distinctions between open and closed-class items . Exploiting notions from current linguistic theory , the... | sobamchan/aclsum | 1 | full_paper |
D09-1085 | This paper introduces a new parser evaluation corpus containing around 700 sentences annotated with unbounded dependencies , from seven different grammatical constructions . We run a series of off-theshelf parsers on the corpus to evaluate how well state-of-the-art parsing technology is able to recover such dependencie... | They propose a new corpus with unbounded dependencies from difference grammatical constructions. | approach | aspect_first | How do the authors address the problem? Provide a brief summary of their approach. Return only the summary in one sentence.
---
Document:
This paper introduces a new parser evaluation corpus containing around 700 sentences annotated with unbounded dependencies , from seven different grammatical constructions . We run ... | sobamchan/aclsum | 1 | full_paper |
E06-1014 | Probabilistic Latent Semantic Analysis ( PLSA ) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis ( LSA ) . However , the parameters of a PLSA model are trained using the Expectation Maximization ( EM ) algorithm , and as a result , the trained model is d... | They show that the model initialized in the proposed method always outperforms existing methods. | outcome | aspect_first | Extract a short summary of the paperโs results and conclusions. Return only the summary in one sentence.
---
Document:
Probabilistic Latent Semantic Analysis ( PLSA ) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis ( LSA ) . However , the parameters of... | sobamchan/aclsum | 2 | full_paper |
D16-1205 | Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words . Current DSMs , however , represent context words as separate features , thereby loosing important information for word expectations , such as word interrelations . In this paper , we present a D... | They show that representations obtained by the proposed model outperform more complex models on two verb similarity datasets with a limited training corpus. | outcome | aspect_first | What are the primary outcomes of the study? Give a concise overview. Return only the summary in one sentence.
---
Document:
Several studies on sentence processing suggest that the mental lexicon keeps track of the mutual expectations between words . Current DSMs , however , represent context words as separate features... | sobamchan/aclsum | 1 | full_paper |
2021.emnlp-main.66 | This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but if someone says " A man is walking a chicken in the park , " it is novel . Given a set of natural language descriptions of normal s... | They propose a fine-grained semantic novelty detection problem where systems detect whether a textual description is a novel fact, coupled with a graph attention-based model. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
This paper proposes to study a fine-grained semantic novelty detection task , which can be illustrated with the following example . It is normal that a person walks a dog in the park , but ... | sobamchan/aclsum | 2 | full_paper |
D09-1066 | Distance-based ( windowless ) word assocation measures have only very recently appeared in the NLP literature and their performance compared to existing windowed or frequency-based measures is largely unknown . We conduct a largescale empirical comparison of a variety of distance-based and frequency-based measures for ... | The performance of new windowless word association measures which take the number of tokens separating words into account remains unknown. | challenge | coverage_first | Give the problem in short form.
---
Document:
Distance-based ( windowless ) word assocation measures have only very recently appeared in the NLP literature and their performance compared to existing windowed or frequency-based measures is largely unknown . We conduct a largescale empirical comparison of a variety of d... | sobamchan/aclsum | 1 | full_paper |
D12-1061 | This paper explores log-based query expansion ( QE ) models for Web search . Three lexicon models are proposed to bridge the lexical gap between Web documents and user queries . These models are trained on pairs of user queries and titles of clicked documents . Evaluations on a real world data set show that the lexicon... | They propose to train lexicon query expansion models by using transaction logs that contain pairs of queries and titles of clicked documents. | approach | aspect_first | Summarize the method or approach the authors propose to tackle the problem. Return only the summary in one sentence.
---
Document:
This paper explores log-based query expansion ( QE ) models for Web search . Three lexicon models are proposed to bridge the lexical gap between Web documents and user queries . These mode... | sobamchan/aclsum | 0 | full_paper |
N15-1159 | This paper describes a simple and principled approach to automatically construct sentiment lexicons using distant supervision . We induce the sentiment association scores for the lexicon items from a model trained on a weakly supervised corpora . Our empirical findings show that features extracted from such a machine-l... | They propose to use Twitter's noisy opinion labels as distant supervision to learn a supervised polarity classifier and use it to obtain sentiment lexicons. | approach | coverage_first | What did the authors do?
---
Document:
This paper describes a simple and principled approach to automatically construct sentiment lexicons using distant supervision . We induce the sentiment association scores for the lexicon items from a model trained on a weakly supervised corpora . Our empirical findings show that ... | sobamchan/aclsum | 2 | full_paper |
2020.aacl-main.88 | Large pre-trained language models reach stateof-the-art results on many different NLP tasks when fine-tuned individually ; They also come with a significant memory and computational requirements , calling for methods to reduce model sizes ( green AI ) . We propose a twostage model-compression method to reduce a model '... | Existing coarse-grained approaches for reducing the inference time of pretraining models remove layers, posing a trade-off between compression and the accuracy of a model. | challenge | coverage_first | State the difficulty of the study.
---
Document:
Large pre-trained language models reach stateof-the-art results on many different NLP tasks when fine-tuned individually ; They also come with a significant memory and computational requirements , calling for methods to reduce model sizes ( green AI ) . We propose a two... | sobamchan/aclsum | 2 | full_paper |
P18-1256 | We introduce the task of predicting adverbial presupposition triggers such as also and again . Solving such a task requires detecting recurring or similar events in the discourse context , and has applications in natural language generation tasks such as summarization and dialogue systems . We create two new datasets f... | The proposed model outperforms baselines including an LSTM-based language model on most of the triggers on the two datasets. | outcome | coverage_first | Give the outcomes in a sentence.
---
Document:
We introduce the task of predicting adverbial presupposition triggers such as also and again . Solving such a task requires detecting recurring or similar events in the discourse context , and has applications in natural language generation tasks such as summarization and... | sobamchan/aclsum | 1 | full_paper |
2021.emnlp-main.765 | The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction ( OpenRE ) . However , high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters can not explicitly align with the relat... | They propose to use available relation labeled data to obtain relation-oriented representation by minimizing the distance between the same relation instances. | approach | coverage_first | Give the approach described.
---
Document:
The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction ( OpenRE ) . However , high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clus... | sobamchan/aclsum | 1 | full_paper |
P03-1015 | The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their combination . An underspecification scheme for the... | They propose several ways to combine a machine learning-based shallow method and a hand-crafted grammar-based cascaded method for parsers. | approach | coverage_first | Summarize the method.
---
Document:
The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their combination .... | sobamchan/aclsum | 0 | full_paper |
E17-1110 | The growing demand for structured knowledge has led to great interest in relation extraction , especially in cases with limited supervision . However , existing distance supervision approaches only extract relations expressed in single sentences . In general , cross-sentence relation extraction is under-explored , even... | Experiments on extracting drug-gene interactions from biomedical literature show that the proposed method doubles the performance of single-sentence extraction methods. | outcome | aspect_first | Extract a short summary of the paperโs results and conclusions. Return only the summary in one sentence.
---
Document:
The growing demand for structured knowledge has led to great interest in relation extraction , especially in cases with limited supervision . However , existing distance supervision approaches only ex... | sobamchan/aclsum | 2 | full_paper |
P19-1252 | In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / following community , and the user 's social... | They extend existing Twitter occupation classification graph-based models to exploit content information by adding textual data to existing datasets. | approach | coverage_first | What did the authors do?
---
Document:
In this paper , we investigate the importance of social network information compared to content information in the prediction of a Twitter user 's occupational class . We show that the content information of a user 's tweets , the profile descriptions of a user 's follower / foll... | sobamchan/aclsum | 2 | full_paper |
2021.naacl-main.150 | A conventional approach to improving the performance of end-to-end speech translation ( E2E-ST ) models is to leverage the source transcription via pre-training and joint training with automatic speech recognition ( ASR ) and neural machine translation ( NMT ) tasks . However , since the input modalities are different ... | They propose a bidirectional sequence knowledge distillation which learns from text-based NMT systems with a single decoder to enhance the model to capture semantic representations. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
A conventional approach to improving the performance of end-to-end speech translation ( E2E-ST ) models is to leverage the source transcription via pre-training and joint training with auto... | sobamchan/aclsum | 2 | full_paper |
N16-1103 | Universal schema builds a knowledge base ( KB ) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns observed in raw text . In most previous applications of universal schema , each textual pattern is represented as a single embedding , preventing generalization to... | They propose to combine universal schemas and neural network-based deep encoders to achieve generalization to an unseen language without additional annotations. | approach | coverage_first | Summarize the method.
---
Document:
Universal schema builds a knowledge base ( KB ) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns observed in raw text . In most previous applications of universal schema , each textual pattern is represented as a single emb... | sobamchan/aclsum | 0 | full_paper |
D15-1054 | Sponsored search is at the center of a multibillion dollar market established by search technology . Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities . Lexical features derived from the text of both the query ... | Conventional word embeddings with a simple integration of click feedback information and averaging to obtain sentence representations do not work well for ad click prediction. | challenge | coverage_first | Give the problem in short form.
---
Document:
Sponsored search is at the center of a multibillion dollar market established by search technology . Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities . Lexical fe... | sobamchan/aclsum | 1 | full_paper |
D17-1222 | We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoderdecoder model equipped with a deep recurrent generative decoder ( DRGN ) . Latent structure information implied in the target summaries is learned based on a recurrent latent random model for improving the summ... | Although humans follow inherent structures in summary writing, currently there are no abstractive summarization models which take latent structure information and recurrent dependencies into account. | challenge | coverage_first | State the difficulty of the study.
---
Document:
We propose a new framework for abstractive text summarization based on a sequence-to-sequence oriented encoderdecoder model equipped with a deep recurrent generative decoder ( DRGN ) . Latent structure information implied in the target summaries is learned based on a re... | sobamchan/aclsum | 2 | full_paper |
D09-1065 | demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI . This could be a very powerful technique for evaluating conceptual models extracted from corpora ; however , fMRI is expensive and imposes strong constraints on data collection . Following on expe... | They propose to use EEG activation patterns instead of fMRI to reduce the cost. | approach | coverage_first | Give the approach described.
---
Document:
demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI . This could be a very powerful technique for evaluating conceptual models extracted from corpora ; however , fMRI is expensive and imposes strong constr... | sobamchan/aclsum | 1 | full_paper |
N18-1114 | We present a new approach to the design of deep networks for natural language processing ( NLP ) , based on the general technique of Tensor Product Representations ( TPRs ) for encoding and processing symbol structures in distributed neural networks . A network architecture -the Tensor Product Generation Network ( TPGN... | They propose a newly designed model that is based on Tensor Product Representations for encoding and processing words and sentences. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
We present a new approach to the design of deep networks for natural language processing ( NLP ) , based on the general technique of Tensor Product Representations ( TPRs ) for encoding and... | sobamchan/aclsum | 2 | full_paper |
P01-1026 | We propose a method to generate large-scale encyclopedic knowledge , which is valuable for much NLP research , based on the Web . We first search the Web for pages containing a term in question . Then we use linguistic patterns and HTML structures to extract text fragments describing the term . Finally , we organize ex... | They propose to use word senses and domains for organizing extracted term descriptions on questions extracted from the Web to improve the quality. | approach | coverage_first | Give the approach described.
---
Document:
We propose a method to generate large-scale encyclopedic knowledge , which is valuable for much NLP research , based on the Web . We first search the Web for pages containing a term in question . Then we use linguistic patterns and HTML structures to extract text fragments de... | sobamchan/aclsum | 1 | full_paper |
P16-1089 | We present the Siamese Continuous Bag of Words ( Siamese CBOW ) model , a neural network for efficient estimation of highquality sentence embeddings . Averaging the embeddings of words in a sentence has proven to be a surprisingly successful and efficient way of obtaining sentence embeddings . However , word embeddings... | While an average of word embeddings has proven to be successful as sentence-level representations, it is suboptimal because they are not optimized to represent sentences. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
We present the Siamese Continuous Bag of Words ( Siamese CBOW ) model , a neural network for efficient estimation of highquality sentence embeddings . Averaging the embeddings of... | sobamchan/aclsum | 1 | full_paper |
P03-1015 | The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their combination . An underspecification scheme for the... | Evaluations on a treebank of German newspaper texts show that the proposed method achieves substantial gain when there are ambiguities. | outcome | coverage_first | Summarize the results.
---
Document:
The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their combination ... | sobamchan/aclsum | 0 | full_paper |
D09-1066 | Distance-based ( windowless ) word assocation measures have only very recently appeared in the NLP literature and their performance compared to existing windowed or frequency-based measures is largely unknown . We conduct a largescale empirical comparison of a variety of distance-based and frequency-based measures for ... | The best windowless measures perform on part with best window-based measures on correlation with human association scores. | outcome | coverage_first | Give the outcomes in a sentence.
---
Document:
Distance-based ( windowless ) word assocation measures have only very recently appeared in the NLP literature and their performance compared to existing windowed or frequency-based measures is largely unknown . We conduct a largescale empirical comparison of a variety of ... | sobamchan/aclsum | 1 | full_paper |
2020.acl-main.282 | The International Classification of Diseases ( ICD ) provides a standardized way for classifying diseases , which endows each disease with a unique code . ICD coding aims to assign proper ICD codes to a medical record . Since manual coding is very laborious and prone to errors , many methods have been proposed for the ... | Existing models that classify texts in medical records into the International Classification of Diseases reduce manual efforts however they ignore Code Hierarchy and Code Co-occurrence. | challenge | aspect_first | Extract a short summary of the core issue that the paper targets. Return only the summary in one sentence.
---
Document:
The International Classification of Diseases ( ICD ) provides a standardized way for classifying diseases , which endows each disease with a unique code . ICD coding aims to assign proper ICD codes ... | sobamchan/aclsum | 2 | full_paper |
D09-1131 | This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences . Chinese words are classified into eight morphological types by two proposed classifiers , CRF classifier and SVM classifier . Experiments show that the injection of morphological information... | They propose to utilize morphological and syntactic features for Chinese opinion analysis on word and sentence levels. | approach | aspect_first | Summarize the method or approach the authors propose to tackle the problem. Return only the summary in one sentence.
---
Document:
This paper employs morphological structures and relations between sentence segments for opinion analysis on words and sentences . Chinese words are classified into eight morphological type... | sobamchan/aclsum | 0 | full_paper |
2021.emnlp-main.765 | The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction ( OpenRE ) . However , high-dimensional vectors can encode complex linguistic information which leads to the problem that the derived clusters can not explicitly align with the relat... | Even though high-dimensional vectors that can encode complex information used for relation extraction are not guaranteed to be consistent with relational semantic similarity. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
The clustering-based unsupervised relation discovery method has gradually become one of the important methods of open relation extraction ( OpenRE ) . However , high-dimensional ... | sobamchan/aclsum | 1 | full_paper |
P12-1013 | Learning entailment rules is fundamental in many semantic-inference applications and has been an active field of research in recent years . In this paper we address the problem of learning transitive graphs that describe entailment rules between predicates ( termed entailment graphs ) . We first identify that entailmen... | Current inefficient algorithms aim to obtain entailment rules for semantic inference hindering the use of large resources. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
---
Document:
Learning entailment rules is fundamental in many semantic-inference applications and has been an active field of research in recent years . In this paper we addres... | sobamchan/aclsum | 0 | full_paper |
D13-1158 | Recent studies on extractive text summarization formulate it as a combinatorial optimization problem such as a Knapsack Problem , a Maximum Coverage Problem or a Budgeted Median Problem . These methods successfully improved summarization quality , but they did not consider the rhetorical relations between the textual u... | The proposed method achieves the highest ROUGE-1,2 scores on 30 documents selected from the RST Discourse Treebank Corpus. | outcome | coverage_first | Summarize the results.
---
Document:
Recent studies on extractive text summarization formulate it as a combinatorial optimization problem such as a Knapsack Problem , a Maximum Coverage Problem or a Budgeted Median Problem . These methods successfully improved summarization quality , but they did not consider the rhet... | sobamchan/aclsum | 0 | full_paper |
2020.acl-main.47 | We examine a methodology using neural language models ( LMs ) for analyzing the word order of language . This LM-based method has the potential to overcome the difficulties existing methods face , such as the propagation of preprocessor errors in count-based methods . In this study , we explore whether the LMbased meth... | They show that language models have sufficient word order knowledge in Japanese to be used as a tool for linguists. | outcome | coverage_first | Summarize the results.
---
Document:
We examine a methodology using neural language models ( LMs ) for analyzing the word order of language . This LM-based method has the potential to overcome the difficulties existing methods face , such as the propagation of preprocessor errors in count-based methods . In this study... | sobamchan/aclsum | 0 | full_paper |
P16-1177 | We present a pairwise context-sensitive Autoencoder for computing text pair similarity . Our model encodes input text into context-sensitive representations and uses them to compute similarity between text pairs . Our model outperforms the state-of-the-art models in two semantic retrieval tasks and a contextual word si... | They propose a pairwise context-sensitive Autoencoder which integrates sentential or document context for computing text pair similarity. | approach | aspect_first | How do the authors address the problem? Provide a brief summary of their approach. Return only the summary in one sentence.
---
Document:
We present a pairwise context-sensitive Autoencoder for computing text pair similarity . Our model encodes input text into context-sensitive representations and uses them to compute... | sobamchan/aclsum | 1 | full_paper |
2020.acl-main.573 | Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in episodic memory and replaying them in subsequent tr... | The proposed method mitigates catastrophic forgetting of old relations and achieves state-of-the-art on several relation extraction datasets showing it can use memorized examples. | outcome | coverage_first | Summarize the results.
---
Document:
Continual relation learning aims to continually train a model on new data to learn incessantly emerging novel relations while avoiding catastrophically forgetting old relations . Some pioneering work has proved that storing a handful of historical relation examples in episodic memo... | sobamchan/aclsum | 0 | full_paper |
D08-1050 | Most state-of-the-art wide-coverage parsers are trained on newspaper text and suffer a loss of accuracy in other domains , making parser adaptation a pressing issue . In this paper we demonstrate that a CCG parser can be adapted to two new domains , biomedical text and questions for a QA system , by using manually-anno... | Most existing parsers are tuned for newspaper texts making them limited in applicable domains. | challenge | coverage_first | What issue is this paper about?
---
Document:
Most state-of-the-art wide-coverage parsers are trained on newspaper text and suffer a loss of accuracy in other domains , making parser adaptation a pressing issue . In this paper we demonstrate that a CCG parser can be adapted to two new domains , biomedical text and que... | sobamchan/aclsum | 0 | full_paper |
P03-1015 | The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their combination . An underspecification scheme for the... | Combining different methods often achieves the best results especially combinations of shallow and deep can realize both interpretability and good results. | challenge | coverage_first | What issue is this paper about?
---
Document:
The paper describes two parsing schemes : a shallow approach based on machine learning and a cascaded finite-state parser with a hand-crafted grammar . It discusses several ways to combine them and presents evaluation results for the two individual approaches and their com... | sobamchan/aclsum | 0 | full_paper |
2020.emnlp-main.308 | Solving algebraic word problems has recently emerged as an important natural language processing task . To solve algebraic word problems , recent studies suggested neural models that generate solution equations by using ' Op ( operator / operand ) ' tokens as a unit of input / output . However , such a neural model suf... | Neural models largely underperform hand-crafted feature-based models on algebraic word datasets such as ALG514 because of two issues namely expression fragmentation and operand-context separation. | challenge | aspect_first | Extract a short summary of the core issue that the paper targets. Return only the summary in one sentence.
---
Document:
Solving algebraic word problems has recently emerged as an important natural language processing task . To solve algebraic word problems , recent studies suggested neural models that generate soluti... | sobamchan/aclsum | 2 | full_paper |
E17-1110 | The growing demand for structured knowledge has led to great interest in relation extraction , especially in cases with limited supervision . However , existing distance supervision approaches only extract relations expressed in single sentences . In general , cross-sentence relation extraction is under-explored , even... | Existing distance supervision methods for relation extraction cannot capture relations crossing the sentence boundary which is important in specialized domains with long-tail knowledge. | challenge | coverage_first | What issue is this paper about?
---
Document:
The growing demand for structured knowledge has led to great interest in relation extraction , especially in cases with limited supervision . However , existing distance supervision approaches only extract relations expressed in single sentences . In general , cross-senten... | sobamchan/aclsum | 0 | full_paper |
D15-1054 | Sponsored search is at the center of a multibillion dollar market established by search technology . Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities . Lexical features derived from the text of both the query ... | Conventional word embeddings with a simple integration of click feedback information and averaging to obtain sentence representations do not work well for ad click prediction. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
---
Document:
Sponsored search is at the center of a multibillion dollar market established by search technology . Accurate ad click prediction is a key component for this marke... | sobamchan/aclsum | 0 | full_paper |
2021.naacl-main.458 | Non-autoregressive Transformer is a promising text generation model . However , current non-autoregressive models still fall behind their autoregressive counterparts in translation quality . We attribute this accuracy gap to the lack of dependency modeling among decoder inputs . In this paper , we propose CNAT , which ... | They propose a non-autoregressive transformer-based model which implicitly learns categorical codes as latent variables into the decoding to complement missing dependencies. | approach | aspect_first | How do the authors address the problem? Provide a brief summary of their approach. Return only the summary in one sentence.
---
Document:
Non-autoregressive Transformer is a promising text generation model . However , current non-autoregressive models still fall behind their autoregressive counterparts in translation ... | sobamchan/aclsum | 1 | full_paper |
D15-1054 | Sponsored search is at the center of a multibillion dollar market established by search technology . Accurate ad click prediction is a key component for this market to function since the pricing mechanism heavily relies on the estimation of click probabilities . Lexical features derived from the text of both the query ... | They propose several joint word embedding methods to leverage positive and negative click feedback which put query vectors close to relevant ad vectors. | approach | aspect_first | How do the authors address the problem? Provide a brief summary of their approach. Return only the summary in one sentence.
---
Document:
Sponsored search is at the center of a multibillion dollar market established by search technology . Accurate ad click prediction is a key component for this market to function sinc... | sobamchan/aclsum | 1 | full_paper |
2020.aacl-main.88 | Large pre-trained language models reach stateof-the-art results on many different NLP tasks when fine-tuned individually ; They also come with a significant memory and computational requirements , calling for methods to reduce model sizes ( green AI ) . We propose a twostage model-compression method to reduce a model '... | They propose a model-compression method which decompresses the matrix and performs feature distillation on the internal representations to recover from the decomposition. | approach | aspect_first | How do the authors address the problem? Provide a brief summary of their approach. Return only the summary in one sentence.
---
Document:
Large pre-trained language models reach stateof-the-art results on many different NLP tasks when fine-tuned individually ; They also come with a significant memory and computational... | sobamchan/aclsum | 1 | full_paper |
2021.acl-long.67 | Bilingual lexicons map words in one language to their translations in another , and are typically induced by learning linear projections to align monolingual word embedding spaces . In this paper , we show it is possible to produce much higher quality lexicons with methods that combine ( 1 ) unsupervised bitext mining ... | Existing methods to induce bilingual lexicons use linear projections to align word embeddings that are based on unrealistic simplifying assumptions. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
Bilingual lexicons map words in one language to their translations in another , and are typically induced by learning linear projections to align monolingual word embedding space... | sobamchan/aclsum | 1 | full_paper |
E17-1060 | We investigate the generation of onesentence Wikipedia biographies from facts derived from Wikidata slot-value pairs . We train a recurrent neural network sequence-to-sequence model with attention to select facts and generate textual summaries . Our model incorporates a novel secondary objective that helps ensure it ge... | They propose a recurrent neural network sequence-to-sequence model with an attention mechanism trained on a multi-task autoencoding objective to generate one-sentence Wikipedia biographies from Wikidata. | approach | aspect_first | How do the authors address the problem? Provide a brief summary of their approach. Return only the summary in one sentence.
---
Document:
We investigate the generation of onesentence Wikipedia biographies from facts derived from Wikidata slot-value pairs . We train a recurrent neural network sequence-to-sequence model... | sobamchan/aclsum | 1 | full_paper |
D18-1065 | In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy , accurate , and efficient attention mechanism for sequence to sequence learning . The method combines the advantage of sharp focus in hard attention and the implementation ease of soft attention . O... | The proposed approach outperforms soft attention models and recent hard attention and Sparsemax models on five translation tasks and also on morphological inflection tasks. | outcome | aspect_first | What are the primary outcomes of the study? Give a concise overview. Return only the summary in one sentence.
---
Document:
In this paper we show that a simple beam approximation of the joint distribution between attention and output is an easy , accurate , and efficient attention mechanism for sequence to sequence le... | sobamchan/aclsum | 1 | full_paper |
2020.emnlp-main.505 | News headline generation aims to produce a short sentence to attract readers to read the news . One news article often contains multiple keyphrases that are of interest to different users , which can naturally have multiple reasonable headlines . However , most existing methods focus on the single headline generation .... | Existing news headline generation models only focus on generating one output even though news articles often have multiple points. | challenge | coverage_first | What issue is this paper about?
---
Document:
News headline generation aims to produce a short sentence to attract readers to read the news . One news article often contains multiple keyphrases that are of interest to different users , which can naturally have multiple reasonable headlines . However , most existing me... | sobamchan/aclsum | 0 | full_paper |
P16-1177 | We present a pairwise context-sensitive Autoencoder for computing text pair similarity . Our model encodes input text into context-sensitive representations and uses them to compute similarity between text pairs . Our model outperforms the state-of-the-art models in two semantic retrieval tasks and a contextual word si... | Existing approaches for textual representation learning only use local information without contexts which capture global information that can guide neural networks in generating accurate representations. | challenge | coverage_first | Give the problem in short form.
---
Document:
We present a pairwise context-sensitive Autoencoder for computing text pair similarity . Our model encodes input text into context-sensitive representations and uses them to compute similarity between text pairs . Our model outperforms the state-of-the-art models in two se... | sobamchan/aclsum | 1 | full_paper |
2020.emnlp-main.500 | Adversarial attacks for discrete data ( such as texts ) have been proved significantly more challenging than continuous data ( such as images ) since it is difficult to generate adversarial samples with gradient-based methods . Current successful attack methods for texts usually adopt heuristic replacement strategies o... | The proposed method outperforms state-of-the-art methods in success rate and perturb percentage while preserving fluency and sematic of generated samples with low cost. | outcome | coverage_first | Describe the conclusions briefly.
---
Document:
Adversarial attacks for discrete data ( such as texts ) have been proved significantly more challenging than continuous data ( such as images ) since it is difficult to generate adversarial samples with gradient-based methods . Current successful attack methods for texts... | sobamchan/aclsum | 2 | full_paper |
D19-1098 | Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks . However , it is unclear what the learned attention captures . The attention computed by attention heads seems not to match human intuitions about hierarchical structures ... | They propose to add an extra constraint to attention heads of the bidirectional Transformer encoder and a module induces tree structures from raw texts. | approach | coverage_first | Give the approach described.
---
Document:
Pre-training Transformer from large-scale raw texts and fine-tuning on the desired task have achieved state-of-the-art results on diverse NLP tasks . However , it is unclear what the learned attention captures . The attention computed by attention heads seems not to match hum... | sobamchan/aclsum | 1 | full_paper |
D12-1011 | Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities . Yet for many types of entities , such as restaurants and cult movies , relational databases exist that contain far more extensive information than Wikipedia . This paper introduces a new task , call... | They propose a task where systems need to reference arbitrary databases for finding named entities not only Wikipedia, together with methods to achieve domain adaptation. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
Existing techniques for disambiguating named entities in text mostly focus on Wikipedia as a target catalog of entities . Yet for many types of entities , such as restaurants and cult movie... | sobamchan/aclsum | 2 | full_paper |
N16-1103 | Universal schema builds a knowledge base ( KB ) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns observed in raw text . In most previous applications of universal schema , each textual pattern is represented as a single embedding , preventing generalization to... | Existing approaches to incorporate universal schemas for automatic knowledge base construction has limitation in generalization to unseen inputs from training time. | challenge | coverage_first | What issue is this paper about?
---
Document:
Universal schema builds a knowledge base ( KB ) of entities and relations by jointly embedding all relation types from input KBs as well as textual patterns observed in raw text . In most previous applications of universal schema , each textual pattern is represented as a ... | sobamchan/aclsum | 0 | full_paper |
P14-1064 | Statistical phrase-based translation learns translation rules from bilingual corpora , and has traditionally only used monolingual evidence to construct features that rescore existing translation candidates . In this work , we present a semi-supervised graph-based approach for generating new translation rules that leve... | Their method significantly improves over existing phrase-based methods on Arabic-English and Urdu-English systems when large language models are used. | outcome | aspect_first | Extract a short summary of the paperโs results and conclusions. Return only the summary in one sentence.
---
Document:
Statistical phrase-based translation learns translation rules from bilingual corpora , and has traditionally only used monolingual evidence to construct features that rescore existing translation cand... | sobamchan/aclsum | 2 | full_paper |
2022.acl-long.304 | Contrastive learning has achieved impressive success in generation tasks to militate the " exposure bias " problem and discriminatively exploit the different quality of references . Existing works mostly focus on contrastive learning on the instance-level without discriminating the contribution of each word , while key... | They propose a CVAE-based hierarchical contrastive learning within instance and keyword-level using a keyword graph which iteratively polishes the keyword representations. | approach | coverage_first | Summarize the method.
---
Document:
Contrastive learning has achieved impressive success in generation tasks to militate the " exposure bias " problem and discriminatively exploit the different quality of references . Existing works mostly focus on contrastive learning on the instance-level without discriminating the ... | sobamchan/aclsum | 0 | full_paper |
2021.acl-long.67 | Bilingual lexicons map words in one language to their translations in another , and are typically induced by learning linear projections to align monolingual word embedding spaces . In this paper , we show it is possible to produce much higher quality lexicons with methods that combine ( 1 ) unsupervised bitext mining ... | The proposed method achieves the state-of-the-art in the bilingual lexical induction task while keeping the interpretability of their pipeline. | outcome | coverage_first | Summarize the results.
---
Document:
Bilingual lexicons map words in one language to their translations in another , and are typically induced by learning linear projections to align monolingual word embedding spaces . In this paper , we show it is possible to produce much higher quality lexicons with methods that com... | sobamchan/aclsum | 0 | full_paper |
D10-1083 | Part-of-speech ( POS ) tag distributions are known to exhibit sparsity -a word is likely to take a single predominant tag in a corpus . Recent research has demonstrated that incorporating this sparsity constraint improves tagging accuracy . However , in existing systems , this expansion come with a steep increase in mo... | Assuming there is only one tag for a word is a powerful heuristic for Part-of-speech tagging but incorporating this into a model leads to complexity. | challenge | coverage_first | Give the problem in short form.
---
Document:
Part-of-speech ( POS ) tag distributions are known to exhibit sparsity -a word is likely to take a single predominant tag in a corpus . Recent research has demonstrated that incorporating this sparsity constraint improves tagging accuracy . However , in existing systems , ... | sobamchan/aclsum | 1 | full_paper |
E06-1014 | Probabilistic Latent Semantic Analysis ( PLSA ) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis ( LSA ) . However , the parameters of a PLSA model are trained using the Expectation Maximization ( EM ) algorithm , and as a result , the trained model is d... | EM algorithm-baed Probabilistic latent semantic analysis models provide high variance in performance and models with different initializations are not comparable. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
---
Document:
Probabilistic Latent Semantic Analysis ( PLSA ) models have been shown to provide a better model for capturing polysemy and synonymy than Latent Semantic Analysis ... | sobamchan/aclsum | 0 | full_paper |
P07-1026 | Convolution tree kernel has shown promising results in semantic role classification . However , it only carries out hard matching , which may lead to over-fitting and less accurate similarity measure . To remove the constraint , this paper proposes a grammardriven convolution tree kernel for semantic role classificatio... | They propose to integrate a linguistically motivated grammar-baed convolution tree kernel into a standard tree kernel to achieve better substructure matching and tree node matching. | approach | coverage_first | Summarize the method.
---
Document:
Convolution tree kernel has shown promising results in semantic role classification . However , it only carries out hard matching , which may lead to over-fitting and less accurate similarity measure . To remove the constraint , this paper proposes a grammardriven convolution tree k... | sobamchan/aclsum | 0 | full_paper |
D07-1036 | Parallel corpus is an indispensable resource for translation model training in statistical machine translation ( SMT ) . Instead of collecting more and more parallel training corpora , this paper aims to improve SMT performance by exploiting full potential of the existing parallel corpora . Two kinds of methods are pro... | Statistical machine translation systems require corpora limited in domain and size, and a model trained on one domain does not perform well on other domains. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
---
Document:
Parallel corpus is an indispensable resource for translation model training in statistical machine translation ( SMT ) . Instead of collecting more and more parall... | sobamchan/aclsum | 0 | full_paper |
E17-1022 | We propose UDP , the first training-free parser for Universal Dependencies ( UD ) . Our algorithm is based on PageRank and a small set of head attachment rules . It features two-step decoding to guarantee that function words are attached as leaf nodes . The parser requires no training , and it is competitive with a del... | The proposed linguistically sound method performs competitively with a delexicalized transfer system while having few parameters and robustness to domain changes across languages. | outcome | coverage_first | Summarize the results.
---
Document:
We propose UDP , the first training-free parser for Universal Dependencies ( UD ) . Our algorithm is based on PageRank and a small set of head attachment rules . It features two-step decoding to guarantee that function words are attached as leaf nodes . The parser requires no train... | sobamchan/aclsum | 0 | full_paper |
N09-1072 | Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue systems and social computing . We describe a system for detecting elements of interactional style : whether a speaker is awkward , friendly , or flirtatious . We create and use a new spoken corpus of 991 4-minut... | Methods to extract social meanings such as engagement from speech remain unknown while it is important in sociolinguistics and to develop socially aware computing systems. | challenge | aspect_first | What is the main research challenge motivating this study? Provide a concise summary. Return only the summary in one sentence.
---
Document:
Automatically extracting social meaning and intention from spoken dialogue is an important task for dialogue systems and social computing . We describe a system for detecting ele... | sobamchan/aclsum | 1 | full_paper |
D09-1065 | demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI . This could be a very powerful technique for evaluating conceptual models extracted from corpora ; however , fMRI is expensive and imposes strong constraints on data collection . Following on expe... | The expensive cost of using fMRI hinders studies on the relationship between corpus-extracted models of semantic knowledge and neural activation patterns. | challenge | aspect_first | Extract a short summary of the core issue that the paper targets. Return only the summary in one sentence.
---
Document:
demonstrated that corpus-extracted models of semantic knowledge can predict neural activation patterns recorded using fMRI . This could be a very powerful technique for evaluating conceptual models ... | sobamchan/aclsum | 2 | full_paper |
P98-1081 | In this paper we examine how the differences in modelling between different data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best individual system . We do this by means of an experiment involving the task of morpho-syntactic wordclass tagging . Four well-known tagge... | They propose to combine four different modelling methods for the task of morpho-syntactic wordclass tagging by using several voting strategies and second stage classifiers. | approach | aspect_first | Outline the methodology used by the authors to solve the stated challenge. Return only the summary in one sentence.
---
Document:
In this paper we examine how the differences in modelling between different data driven systems performing the same NLP task can be exploited to yield a higher accuracy than the best indivi... | sobamchan/aclsum | 2 | full_paper |
D09-1030 | Manual evaluation of translation quality is generally thought to be excessively time consuming and expensive . We explore a fast and inexpensive way of doing it using Amazon 's Mechanical Turk to pay small sums to a large number of non-expert annotators . For $ 10 we redundantly recreate judgments from a WMT08 translat... | Because of the high cost required for manual evaluation, most works rely on automatic evaluation metrics although there are several drawbacks. | challenge | aspect_first | From the following paper excerpt, summarize the key problem or challenge the authors aim to address. Return only the summary in one sentence.
---
Document:
Manual evaluation of translation quality is generally thought to be excessively time consuming and expensive . We explore a fast and inexpensive way of doing it us... | sobamchan/aclsum | 0 | full_paper |
2020.emnlp-main.500 | Adversarial attacks for discrete data ( such as texts ) have been proved significantly more challenging than continuous data ( such as images ) since it is difficult to generate adversarial samples with gradient-based methods . Current successful attack methods for texts usually adopt heuristic replacement strategies o... | The proposed method outperforms state-of-the-art methods in success rate and perturb percentage while preserving fluency and sematic of generated samples with low cost. | outcome | aspect_first | Summarize the main results or outcomes reported by the authors. Return only the summary in one sentence.
---
Document:
Adversarial attacks for discrete data ( such as texts ) have been proved significantly more challenging than continuous data ( such as images ) since it is difficult to generate adversarial samples wi... | sobamchan/aclsum | 0 | full_paper |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.