83 research outputs found
ParaNMT-50M: Pushing the Limits of Paraphrastic Sentence Embeddings with Millions of Machine Translations
We describe PARANMT-50M, a dataset of more than 50 million English-English
sentential paraphrase pairs. We generated the pairs automatically by using
neural machine translation to translate the non-English side of a large
parallel corpus, following Wieting et al. (2017). Our hope is that ParaNMT-50M
can be a valuable resource for paraphrase generation and can provide a rich
source of semantic knowledge to improve downstream natural language
understanding tasks. To show its utility, we use ParaNMT-50M to train
paraphrastic sentence embeddings that outperform all supervised systems on
every SemEval semantic textual similarity competition, in addition to showing
how it can be used for paraphrase generation
Methods of sentence extraction, abstraction and ordering for automatic text summarization
In this thesis, we have developed several techniques for tackling both the extractive and abstractive text summarization tasks. We implement a rank based extractive sentence selection algorithm. For ensuring a pure sentence abstraction, we propose several novel sentence abstraction techniques which jointly perform sentence compression, fusion, and paraphrasing at the sentence level. We also model abstractive compression generation as a sequence-to-sequence (seq2seq) problem using an encoder-decoder framework. Furthermore, we applied our sentence abstraction techniques to the multi-document abstractive text summarization. We also propose a greedy sentence ordering algorithm to maintain the summary coherence for increasing the readability. We introduce an optimal solution to the summary length limit problem. Our experiments demonstrate that the methods bring significant improvements over the state-of-the-art methods. At the end of this thesis, we also introduced a new concept called "Reader Aware Summary" which can generate summaries for some critical readers (e.g. Non-Native Reader).Natural Sciences and Engineering Research Council (NSERC) of Canada and the University of Lethbridg
Sentence Similarity and Machine Translation
Neural machine translation (NMT) systems encode an input sentence into an intermediate representation and then decode that representation into the output sentence. Translation requires deep understanding of language; as a result, NMT models trained on large amounts of data develop a semantically rich intermediate representation.
We leverage this rich intermediate representation of NMT systems—in particular, multilingual NMT systems, which learn to map many languages into and out of a joint space—for bitext curation, paraphrasing, and automatic machine translation (MT) evaluation. At a high level, all of these tasks are rooted in similarity: sentence and document alignment requires measuring similarity of sentences and documents, respectively; paraphrasing requires producing output which is similar to an input; and automatic MT evaluation requires measuring the similarity between MT system outputs and corresponding human reference translations.
We use multilingual NMT for similarity in two ways: First, we use a multilingual NMT model with a fixed-size intermediate representation (Artetxe and Schwenk, 2018) to produce multilingual sentence embeddings, which we use in both sentence and document alignment. Second, we train a multilingual NMT model and show that it generalizes to the task of generative paraphrasing (i.e., “translating” from Russian to Russian), when used in conjunction with a simple generation algorithm to discourage copying from the input to the output. We also use this model for automatic MT evaluation, to force decode and score MT system outputs conditioned on their respective human reference translations. Since we leverage multilingual NMT models, each method works in many languages using a single model.
We show that simple methods, which leverage the intermediate representation of multilingual NMT models trained on large amounts of bitext, outperform prior work in paraphrasing, sentence alignment, document alignment, and automatic MT evaluation. This finding is consistent with recent trends in the natural language processing community, where large language models trained on huge amounts of unlabeled text have achieved state-of-the-art results on tasks such as question answering, named entity recognition, and parsing
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense
The rise in malicious usage of large language models, such as fake content
creation and academic plagiarism, has motivated the development of approaches
that identify AI-generated text, including those based on watermarking or
outlier detection. However, the robustness of these detection algorithms to
paraphrases of AI-generated text remains unclear. To stress test these
detectors, we build a 11B parameter paraphrase generation model (DIPPER) that
can paraphrase paragraphs, condition on surrounding context, and control
lexical diversity and content reordering. Using DIPPER to paraphrase text
generated by three large language models (including GPT3.5-davinci-003)
successfully evades several detectors, including watermarking, GPTZero,
DetectGPT, and OpenAI's text classifier. For example, DIPPER drops detection
accuracy of DetectGPT from 70.3% to 4.6% (at a constant false positive rate of
1%), without appreciably modifying the input semantics.
To increase the robustness of AI-generated text detection to paraphrase
attacks, we introduce a simple defense that relies on retrieving
semantically-similar generations and must be maintained by a language model API
provider. Given a candidate text, our algorithm searches a database of
sequences previously generated by the API, looking for sequences that match the
candidate text within a certain threshold. We empirically verify our defense
using a database of 15M generations from a fine-tuned T5-XXL model and find
that it can detect 80% to 97% of paraphrased generations across different
settings while only classifying 1% of human-written sequences as AI-generated.
We open-source our models, code and data.Comment: NeurIPS 2023 camera ready (32 pages). Code, models, data available in
https://github.com/martiansideofthemoon/ai-detection-paraphrase
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