update index

.gitattributes

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+index filter=lfs diff=lfs merge=lfs -text
+papers.json filter=lfs diff=lfs merge=lfs -text
+index/*.pt filter=lfs diff=lfs merge=lfs -text
+index/*.json filter=lfs diff=lfs merge=lfs -text

README.md

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----
-license: apache-2.0
----
-
-Use ColBERT as a search engine for the [ACL Anthology](https://aclanthology.org/). (Parse any bibtex, and store in a MySQL service).
-
-**This repo contains the ColBERT index and dataset.** To run the interface, see [github.com/davidheineman/acl-search](https://github.com/davidheineman/acl-search).
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index/0.codes.pt

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index/0.metadata.json

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index/2.metadata.json

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+}

anthology.bib → index/2.residuals.pt

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index/avg_residual.pt

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index/doclens.2.json

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index/ivf.pid.pt

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:cae73783e14aec903b9ba883b458e16dc152dbbb12d4a68247a69910c28bfc6d
-size 23087591
+oid sha256:b8072ef751792e4725af0a5a26730591160b6d775520aa01206cf1886b129228
+size 34645095

index/metadata.json

@@ -1,3 +1,62 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:b4e0e16848c474ae0ea7a3f235dbe98aff3f0086acd5bb0b7bd5caf8c44d9d34
-size 4073
+{
+    "config": {
+        "query_token_id": "[unused0]",
+        "doc_token_id": "[unused1]",
+        "query_token": "[Q]",
+        "doc_token": "[D]",
+        "ncells": null,
+        "centroid_score_threshold": null,
+        "ndocs": null,
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+        "index_path": "index",
+        "nbits": 2,
+        "kmeans_niters": 4,
+        "resume": false,
+        "similarity": "cosine",
+        "bsize": 64,
+        "accumsteps": 1,
+        "lr": 3e-6,
+        "maxsteps": 500000,
+        "save_every": null,
+        "warmup": null,
+        "warmup_bert": null,
+        "relu": false,
+        "nway": 2,
+        "use_ib_negatives": false,
+        "reranker": false,
+        "distillation_alpha": 1.0,
+        "ignore_scores": false,
+        "model_name": null,
+        "query_maxlen": 32,
+        "attend_to_mask_tokens": false,
+        "interaction": "colbert",
+        "dim": 128,
+        "doc_maxlen": 512,
+        "mask_punctuation": true,
+        "checkpoint": "colbert-ir\/colbertv2.0",
+        "triples": null,
+        "collection": [
+            "list with 67577 elements starting with...",
+            [
+                "In recent years, several end-to-end online translation systems have been proposed to successfully incorporate human post-editing feedback in the translation workflow. The performance of these systems in a multi-domain translation environment (involving different text genres, post-editing styles, machine translation systems) within the automatic post-editing (APE) task has not been thoroughly investigated yet. In this work, we show that when used in the APE framework the existing online systems are not robust towards domain changes in the incoming data stream. In particular, these systems lack in the capability to learn and use domain-specific post-editing rules from a pool of multi-domain data sets. To cope with this problem, we propose an online learning framework that generates more reliable translations with significantly better quality as compared with the existing online and batch systems. Our framework includes: i) an instance selection technique based on information retrieval that helps to build domain-specific APE systems, and ii) an optimization procedure to tune the feature weights of the log-linear model that allows the decoder to improve the post-editing quality.",
+                "We assessed how different machine translation (MT) systems affect the post-editing (PE) process and product of professional English\u2013Spanish translators. Our model found that for each 1-point increase in BLEU, there is a PE time decrease of 0.16 seconds per word, about 3-4%. The MT system with the lowest BLEU score produced the output that was post-edited to the lowest quality and with the highest PE effort, measured both in HTER and actual PE operations.",
+                "Computer-aided translation (CAT) tools often use a translation memory (TM) as the key resource to assist translators. A TM contains translation units (TU) which are made up of source and target language segments; translators use the target segments in the TU suggested by the CAT tool by converting them into the desired translation. Proposals from TMs could be made more useful by using techniques such as fuzzy-match repair (FMR) which modify words in the target segment corresponding to mismatches identified in the source segment. Modifications in the target segment are done by translating the mismatched source sub-segments using an external source of bilingual information (SBI) and applying the translations to the corresponding positions in the target segment. Several combinations of translated sub-segments can be applied to the target segment which can produce multiple repair candidates. We provide a formal algorithmic description of a method that is capable of using any SBI to generate all possible fuzzy-match repairs and perform an oracle evaluation on three different language pairs to ascertain the potential of the method to improve translation productivity. Using DGT-TM translation memories and the machine system Apertium as the single source to build repair operators in three different language pairs, we show that the best repaired fuzzy matches are consistently closer to reference translations than either machine-translated segments or unrepaired fuzzy matches."
+            ]
+        ],
+        "queries": null,
+        "index_name": "index",
+        "overwrite": false,
+        "root": "\/coc\/pskynet6\/dheineman3\/colbert-acl\/acl-search\/experiments",
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+        "index_root": null,
+        "name": "2024-08\/24\/01.35.22",
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+}

index/plan.json

@@ -36,27 +36,27 @@
         "checkpoint": "colbert-ir\/colbertv2.0",
         "triples": null,
         "collection": [
-            "list with 47683 elements starting with...",
+            "list with 67577 elements starting with...",
             [
-                "This paper studies the correction of challenging authentic Finnish learner texts at beginner level (CEFR A1). Three state-of-the-art large language models are compared, and it is shown that GPT-4 outperforms GPT-3.5, which in turn outperforms Claude v1 on this task. Additionally, ensemble models based on classifiers combining outputs of multiple single models are evaluated. The highest accuracy for an ensemble model is 84.3{\\%}, whereas the best single model, which is a GPT-4 model, produces sentences that are fully correct 83.3{\\%} of the time. In general, the different models perform on a continuum, where grammatical correctness, fluency and coherence go hand in hand.",
-                "In recent years, large pre-trained language models (PLMs) have achieved remarkable performance on many natural language processing benchmarks. Despite their success, prior studies have shown that PLMs are vulnerable to attacks from adversarial examples. In this work, we focus on the named entity recognition task and study context-aware adversarial attack methods to examine the model{'}s robustness. Specifically, we propose perturbing the most informative words for recognizing entities to create adversarial examples and investigate different candidate replacement methods to generate natural and plausible adversarial examples. Experiments and analyses show that our methods are more effective in deceiving the model into making wrong predictions than strong baselines.",
-                "This paper investigates effects of noisy source texts (containing spelling and grammar errors, informal words or expressions, etc.) on human and machine translations, namely whether the noisy phenomena are kept in the translations, corrected, or caused errors. The analysed data consists of English user reviews of Amazon products translated into Croatian, Russian and Finnish by professional translators, translation students, machine translation (MT) systems, and ChatGPT language model. The results show that overall, ChatGPT and professional translators mostly correct\/standardise those parts, while students are often keeping them. Furthermore, MT systems are most prone to errors while ChatGPT is more robust, but notably less robust than human translators. Finally, some of the phenomena are particularly challenging both for MT systems and for ChatGPT, especially spelling errors and informal constructions."
+                "In recent years, several end-to-end online translation systems have been proposed to successfully incorporate human post-editing feedback in the translation workflow. The performance of these systems in a multi-domain translation environment (involving different text genres, post-editing styles, machine translation systems) within the automatic post-editing (APE) task has not been thoroughly investigated yet. In this work, we show that when used in the APE framework the existing online systems are not robust towards domain changes in the incoming data stream. In particular, these systems lack in the capability to learn and use domain-specific post-editing rules from a pool of multi-domain data sets. To cope with this problem, we propose an online learning framework that generates more reliable translations with significantly better quality as compared with the existing online and batch systems. Our framework includes: i) an instance selection technique based on information retrieval that helps to build domain-specific APE systems, and ii) an optimization procedure to tune the feature weights of the log-linear model that allows the decoder to improve the post-editing quality.",
+                "We assessed how different machine translation (MT) systems affect the post-editing (PE) process and product of professional English\u2013Spanish translators. Our model found that for each 1-point increase in BLEU, there is a PE time decrease of 0.16 seconds per word, about 3-4%. The MT system with the lowest BLEU score produced the output that was post-edited to the lowest quality and with the highest PE effort, measured both in HTER and actual PE operations.",
+                "Computer-aided translation (CAT) tools often use a translation memory (TM) as the key resource to assist translators. A TM contains translation units (TU) which are made up of source and target language segments; translators use the target segments in the TU suggested by the CAT tool by converting them into the desired translation. Proposals from TMs could be made more useful by using techniques such as fuzzy-match repair (FMR) which modify words in the target segment corresponding to mismatches identified in the source segment. Modifications in the target segment are done by translating the mismatched source sub-segments using an external source of bilingual information (SBI) and applying the translations to the corresponding positions in the target segment. Several combinations of translated sub-segments can be applied to the target segment which can produce multiple repair candidates. We provide a formal algorithmic description of a method that is capable of using any SBI to generate all possible fuzzy-match repairs and perform an oracle evaluation on three different language pairs to ascertain the potential of the method to improve translation productivity. Using DGT-TM translation memories and the machine system Apertium as the single source to build repair operators in three different language pairs, we show that the best repaired fuzzy matches are consistently closer to reference translations than either machine-translated segments or unrepaired fuzzy matches."
             ]
         ],
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         "index_name": "index",
         "overwrite": false,
-        "root": "\/coc\/pskynet6\/dheineman3\/4440\/colbert-acl\/src\/experiments",
+        "root": "\/coc\/pskynet6\/dheineman3\/colbert-acl\/acl-search\/experiments",
         "experiment": "notebook",
         "index_root": null,
-        "name": "2024-08\/11\/23.16.25",
+        "name": "2024-08\/24\/01.35.22",
         "rank": 0,
         "nranks": 2,
         "amp": true,
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+        "gpus": 4
     },
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+    "num_chunks": 3,
     "num_partitions": 32768,
-    "num_embeddings_est": 8137376.50328064,
-    "avg_doclen_est": 170.6557159423828
+    "num_embeddings_est": 12161657.478149414,
+    "avg_doclen_est": 179.9674072265625
 }

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