wanyu/IteraTeR_full_sent huggingface dataset:
Original:  Given a set of input terms, we assign a score for the likelihood that each pair of terms forms a parent-child relation . To produce a tree from pairwise parent-child edge scores, we treat this as a graph optimization problem and output the maximum spanning tree  .
Revised:  Given a set of input terms, we assign a score for the likelihood that each pair of terms forms a parent-child relation scores. The tree reconciliation module treats the task as a graph optimization problem and output the maximum spanning tree .
Edit intent: clarity
Original:  We train the model by finetuning it on parent-child relations from subtrees of WordNet and test on non-overlapping subtrees. In addition, we incorporate semi-structured definitions from the web to further improve performance.
Revised:  We train the model by finetuning it on parent-child relations from subtrees of WordNet and test on non-overlapping WordNet subtrees. We show that incorporating web-retrieved glosses can further improve performance.
Edit intent: clarity
Original:  On the task of inducing subtrees of WordNet, the model achieves 66.0 ancestor F_1, a 10.4 point absolute increase over the previous best published result on this task  .
Revised:  On the task of constructing subtrees of English WordNet, the model achieves 66.0 ancestor F_1, a 10.4 point absolute increase over the previous best published result on this task .
Edit intent: meaning-changed
Original:  On the task of inducing subtrees of WordNet, the model achieves 66.0 ancestor F_1, a 10.4 point absolute increase over the previous best published result on this task  .
Revised:  On the task of inducing subtrees of WordNet, the model achieves 66.7 ancestor F1, a 20.0\% relative increase over the previous best published result on this task .
Edit intent: meaning-changed
Original:  On the task of inducing subtrees of WordNet, the model achieves 66.0 ancestor F_1, a 10.4 point absolute increase over the previous best published result on this task  .
Revised:  On the task of inducing subtrees of WordNet, the model achieves 66.0 ancestor F_1, a 10.4 point absolute increase over the previous best published result on this task . In addition, we convert the original English dataset into nine other languages using Open Multilingual WordNet and extend our results across these languages .
Edit intent: meaning-changed
Original: We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task.
Revised: We present a simple yet effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task.
Edit intent: clarity
Original: We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task.
Revised: We propose an effective approach to build multilingual speech-to-text translation (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task.
Edit intent: clarity
Original: We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task.
Revised: We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task.
Edit intent: clarity
Original: We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST) . Our recipe to achieve cross-modal and cross-lingual transfer learning (XMTL) is simple and generalizable: using an adaptor module to bridge the modules pretrained in different modalities, and an efficient finetuning step which leverages the knowledge from pretrained modules yet making it work on a drastically different downstream task. With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Revised: We propose an effective approach to utilize pretrained speech and text models to perform speech-to-text translation (ST) translation by efficient transfer learning from pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve crosslingual and cross-modality transfer ability with only finetuning less than 10\% of the pretrained parameters. This enables effectively leveraging large pretrained models with low training cost. Using wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Edit intent: clarity
Original:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Revised:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Edit intent: meaning-changed
Original:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Revised:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Edit intent: meaning-changed
Original:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Revised:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark %DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Edit intent: coherence
Original:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Revised:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 ( + 2.8 BLEU and + 3.9 BLEU , respectively.
Edit intent: meaning-changed
Original:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively.
Revised:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 4.4 BLEU on average for 13 En-X directions and + 3.9 BLEU , respectively.
Edit intent: meaning-changed
Original:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 3.9 BLEU , respectively. On low-resource languages (with less than 10 hours training data), our approach significantly improves the quality of speech-to-text translation with +9.0 BLEU on Portuguese-English and +5.2 BLEU on Dutch-English .
Revised:  With this approach, we built a multilingual speech-to-text translation model with pretrained audio encoder ( wav2vec ) and multilingual text decoder (mBART), which achieves new state-of-the-art on CoVoST 2 ST benchmark 1%DIFDELCMD < ] %%% for English into 15 languages as well as 6 Romance languages into English with on average + 2.8 BLEU and + 4.3 BLEU on average for 19 X-En directions). Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model, making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency .
Edit intent: meaning-changed
Original:  Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve  crosslingual and cross-modality transfer ability with only finetuning less than 10\% of the pretrained parameters.
Revised:  Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability with only finetuning less than 10\% of the pretrained parameters.
Edit intent: meaning-changed
Original:  Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve  crosslingual and cross-modality transfer ability with only finetuning less than 10\% of the pretrained parameters.
Revised:  Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve crosslingual and cross-modality transfer ability by only finetuning less than 10\% of the pretrained parameters.
Edit intent: fluency
Original:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Revised:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 34 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Edit intent: meaning-changed
Original:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Revised:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 23 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Edit intent: meaning-changed
Original:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Revised:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on large-scale multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Edit intent: clarity
Original:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Revised:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 6.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Edit intent: meaning-changed
Original:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Revised:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average across 15 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Edit intent: meaning-changed
Original:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Revised:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 5.1 BLEU on average for 19 X-En directions).
Edit intent: meaning-changed
Original:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average for 19 X-En directions).
Revised:  Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 32 translation directions (and surpassing cascaded ST for 19 of them) on the multilingual ST benchmark CoVoST 2 (+ 4.4 BLEU on average for 13 En-X directions and + 4.3 BLEU on average across 19 X-En directions).
Edit intent: clarity
Original:  Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model  , making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency.
Revised:  Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model , making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency.
Edit intent: clarity
Original:  Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model  , making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency.
Revised:  Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model (+5.7 BLEU on average across 18 non-English directions) , making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency.
Edit intent: meaning-changed
Original:  Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model  , making it an appealing approach for attaining high-quality speech translation with low training cost and improved data efficiency.
Revised:  Our approach demonstrates strong performance in both zero-shot crosslingual transfer and zero-shot pair-wise translation in a many-to-many multilingual model , making it an appealing approach for attaining high-quality speech translation with improved parameter and data efficiency.
Edit intent: clarity
Original: Text generation models can generate factually inconsistent text containing distorted or fabricated facts about the source text. Recent work has focused on building evaluation models to verify the factual correctness of semantically constrained text generation tasks such as document summarization. While the field of factuality evaluation is growing fast, we don't have well-defined criteria for measuring the effectiveness, generalizability, reliability, or sensitivity of the factuality metrics. Focusing on these aspects, in this paper, we introduce  a meta-evaluation framework for evaluating factual consistency metrics.
Revised: Text generation models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This major discrepancy between the surface-level fluency and the content-level correctness of neural generation has motivated a new line of research that seeks automatic metrics for evaluating the factuality of machine text. In this paper, we introduce a meta-evaluation framework for evaluating factual consistency metrics.
Edit intent: clarity
Original:  We introduce five necessary , common-sense conditions for effective factuality metrics and experiment with nine recent factuality metrics using synthetic and human-labeled factuality data from short news, long news and dialogue summarization domains. Our framework enables assessing the efficiency of any new factual consistency metric on a variety of dimensions over multiple summarization domains and can be easily extended with new meta-evaluation criteria. We also present our conclusions towards standardizing the factuality evaluation metrics .
Revised:  We propose five necessary and intuitive conditions to evaluate factuality metrics on diagnostic factuality data across three different summarization tasks. Our benchmark analysis on ten factuality metrics reveals that our meta-evaluation framework provides a robust and efficient evaluation that is extensible to multiple types of factual consistency and standard generation metrics, including QA metrics. It also reveals that while QA metrics generally improve over standard metrics that measure factuality across domains, performance is highly dependent on the way in which questions are generated .
Edit intent: coherence
Original: AI assistants have started carrying out tasks on a user's behalf by interacting directly with the web. However, training an interface that maps natural language (NL) commands to web actions is challenging for existing semantic parsing approaches due to the variable and unknown set of actions that characterize websites .
Revised: AI assistants can now carry out tasks for users by directly interacting with website UIs. Current semantic parsing and slot-filling techniques cannot flexibly adapt to many different websites without being constantly re-trained .
Edit intent: clarity
Original:  We propose FLIN, a natural language interface for web navigation that maps NL commands to concept-level actions  rather than low-level UI interactions , thus being able to flexibly adapt to different websites and handle their transient nature.
Revised:  We propose FLIN, a natural language interface for web navigation that maps user commands to concept-level actions rather than low-level UI interactions , thus being able to flexibly adapt to different websites and handle their transient nature.
Edit intent: fluency
Original:  We propose FLIN, a natural language interface for web navigation that maps NL commands to concept-level actions  rather than low-level UI interactions , thus being able to flexibly adapt to different websites and handle their transient nature.
Revised:  We propose FLIN, a natural language interface for web navigation that maps NL commands to concept-level actions ( rather than low-level UI interactions , thus being able to flexibly adapt to different websites and handle their transient nature.
Edit intent: fluency